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03c6393e-dfe0-4a9a-9c0b-f881c617fa33 | a-bayesian-sparse-factor-model-with-adaptive | 2305.18488 | null | https://arxiv.org/abs/2305.18488v1 | https://arxiv.org/pdf/2305.18488v1.pdf | A Bayesian sparse factor model with adaptive posterior concentration | In this paper, we propose a new Bayesian inference method for a high-dimensional sparse factor model that allows both the factor dimensionality and the sparse structure of the loading matrix to be inferred. The novelty is to introduce a certain dependence between the sparsity level and the factor dimensionality, which ... | ['Yongdai Kim', 'Lizhen Lin', 'Ilsang Ohn'] | 2023-05-29 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.13319442e-01 -1.37771070e-02 -4.27289277e-01 1.78101942e-01
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-1.40260041e-01 3.12474310e-01 -4.08411026e-02 2.54565388... | [7.046165943145752, 4.5426459312438965] |
5e0a437f-29a6-4ae2-9bf2-11bc499b8065 | towards-building-text-to-speech-systems-for | 2211.09536 | null | https://arxiv.org/abs/2211.09536v3 | https://arxiv.org/pdf/2211.09536v3.pdf | Towards Building Text-To-Speech Systems for the Next Billion Users | Deep learning based text-to-speech (TTS) systems have been evolving rapidly with advances in model architectures, training methodologies, and generalization across speakers and languages. However, these advances have not been thoroughly investigated for Indian language speech synthesis. Such investigation is computatio... | ['Karthik Nandakumar', 'Mitesh M. Khapra', 'Pratyush Kumar', 'Praveen S V', 'Gokul Karthik Kumar'] | 2022-11-17 | null | null | null | null | ['speech-synthesis-bodo', 'speech-synthesis-rajasthani', 'speech-synthesis-marathi', 'speech-synthesis-kannada', 'text-to-speech-synthesis', 'speech-synthesis-odia', 'speech-synthesis-manipuri', 'speech-synthesis-bengali', 'speech-synthesis-assamese', 'speech-synthesis-hindi', 'speech-synthesis-gujarati', 'speech-synth... | ['speech', 'speech', 'speech', 'speech', 'speech', 'speech', 'speech', 'speech', 'speech', 'speech', 'speech', 'speech', 'speech', 'speech'] | [-2.48201385e-01 -1.13367334e-01 -1.16168279e-02 -5.22112370e-01
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1.58394054e-01 7.08839536e-01 -1.40325740e-01 -4.04819131... | [14.645650863647461, 6.852306365966797] |
697b939c-430d-4e59-b101-71c4d8ec7c77 | a-novel-strategy-for-improving-robustness-in | 2305.09407 | null | https://arxiv.org/abs/2305.09407v1 | https://arxiv.org/pdf/2305.09407v1.pdf | A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection | Visual quality inspection in high performance manufacturing can benefit from automation, due to cost savings and improved rigor. Deep learning techniques are the current state of the art for generic computer vision tasks like classification and object detection. Manufacturing data can pose a challenge for deep learning... | ['Andrew E. Marble', 'Ahmad Mohamad Mezher'] | 2023-05-16 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 5.00842571e-01 -6.86482489e-02 3.24334681e-01 -5.28983712e-01
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f718e62a-08e2-44fc-a74e-c65e398814c7 | gental-generative-denoising-skip-gram | null | null | https://openreview.net/forum?id=36SHWj0Gp1 | https://openreview.net/pdf?id=36SHWj0Gp1 | GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection | Binary code similarity detection serves a critical role in cybersecurity. It alleviates the huge manual effort required in the reverse engineering process for malware analysis and vulnerability detection, where often the original source code is not available for analysis. Most of the existing solutions focus on a manua... | ['Christopher James Molloy', 'Hanbo Yu', 'Philippe Charland', 'Steven Ding', 'Litao Li'] | 2021-09-29 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 2.25701138e-01 -4.12780493e-01 -4.30699646e-01 -5.20236731e-01
-7.89480269e-01 -7.44151950e-01 4.08839077e-01 4.67777431e-01
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1.35442009e-03 9.22259241e-02 2.77666688e-01 -2.14837000... | [7.127371788024902, 7.808948993682861] |
f05543c7-00bf-4888-85cf-19eea67f96ca | dit-3d-exploring-plain-diffusion-transformers | 2307.01831 | null | https://arxiv.org/abs/2307.01831v1 | https://arxiv.org/pdf/2307.01831v1.pdf | DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation | Recent Diffusion Transformers (e.g., DiT) have demonstrated their powerful effectiveness in generating high-quality 2D images. However, it is still being determined whether the Transformer architecture performs equally well in 3D shape generation, as previous 3D diffusion methods mostly adopted the U-Net architecture. ... | ['Zhenguo Li', 'Matthias Nießner', 'Lanqing Hong', 'Lewei Yao', 'Ruihang Chu', 'Enze Xie', 'Shentong Mo'] | 2023-07-04 | null | null | null | null | ['3d-shape-generation', 'point-cloud-generation', 'philosophy'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [-1.38535142e-01 2.08437100e-01 3.24858129e-01 -1.39363781e-01
-9.98453796e-01 -3.73483360e-01 6.24192536e-01 -2.95137912e-01
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5.21262214e-02 -1.49504578e+00 -1.14740586e+00 -5.67650974e-01
1.79696187e-01 7.10845768e-01 2.20567912e-01 -4.08236295... | [8.856958389282227, -3.659865617752075] |
fc4a0c2e-2d33-40b0-aab1-84201fba9fc9 | domain-adaptive-transfer-learning-on-visual | 2010.03071 | null | https://arxiv.org/abs/2010.03071v1 | https://arxiv.org/pdf/2010.03071v1.pdf | Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization | Fine-Grained Visual Categorization (FGVC) is a challenging topic in computer vision. It is a problem characterized by large intra-class differences and subtle inter-class differences. In this paper, we tackle this problem in a weakly supervised manner, where neural network models are getting fed with additional data us... | ['Vassilis Athitsos', 'Ashiq Imran'] | 2020-10-06 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 5.06470203e-02 -3.52348804e-01 -2.87627906e-01 -5.32671630e-01
-5.02075076e-01 -7.91516125e-01 8.22493017e-01 -7.25500286e-02
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1.12391599e-01 4.82097864e-01 3.04834008e-01 -2.45834053... | [9.606483459472656, 2.055370807647705] |
82fb2a4f-acb3-4622-932a-60c36ec3ebf8 | latentgan-autoencoder-learning-disentangled | 2204.02010 | null | https://arxiv.org/abs/2204.02010v1 | https://arxiv.org/pdf/2204.02010v1.pdf | LatentGAN Autoencoder: Learning Disentangled Latent Distribution | In autoencoder, the encoder generally approximates the latent distribution over the dataset, and the decoder generates samples using this learned latent distribution. There is very little control over the latent vector as using the random latent vector for generation will lead to trivial outputs. This work tries to add... | ['Tanay Dixit', 'Animikh Aich', 'Sanket Kalwar'] | 2022-04-05 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [-9.34743136e-02 7.97939718e-01 -3.30531865e-01 -1.22466624e-01
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2.84983307e-01 6.16287231e-01 -3.88807058e-01 2.07874849... | [11.550790786743164, -0.06229942664504051] |
482179f0-27ae-4d59-9718-bcd06e1bb28a | a-robust-and-low-complexity-deep-learning | 2211.02820 | null | https://arxiv.org/abs/2211.02820v2 | https://arxiv.org/pdf/2211.02820v2.pdf | A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification | In this paper, we present a robust and low complexity deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the scene of a remote sensing image. In particular, we firstly evaluate different low complexity and benchmark deep neural networks: MobileNetV1, MobileNetV2, NASNetMobile, a... | ['Le Hong Trang', 'Truong Nguyen', 'Nghia NVN', 'Lam Pham', 'Cam Le'] | 2022-11-05 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 2.12328121e-01 -4.68495518e-01 -1.37400061e-01 -4.95508164e-01
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-2.46848300e-01 2.13828823e-03 2.42340431e-01 1.96842253... | [9.233260154724121, -0.6052197217941284] |
b23bea62-4ea3-4f85-a039-a9475ca4c6cc | minimum-error-tree-decomposition | 1304.1103 | null | http://arxiv.org/abs/1304.1103v1 | http://arxiv.org/pdf/1304.1103v1.pdf | Minimum Error Tree Decomposition | This paper describes a generalization of previous methods for constructing
tree-structured belief network with hidden variables. The major new feature of
the described method is the ability to produce a tree decomposition even when
there are errors in the correlation data among the input variables. This is an
important... | ['X. Ying', 'Y. Ma', 'D. Wilkins', 'L. Liu', 'Z. Bian'] | 2013-03-27 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-8.60828683e-02 2.73707777e-01 -3.48233104e-01 -6.32780015e-01
-2.77321041e-01 -4.08159159e-02 2.47603089e-01 -5.02191577e-03
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-5.00976145e-01 -1.06020725e+00 -2.85450295e-02 -8.85724187e-01
-2.81964332e-01 9.28679705e-01 3.44707459e-01 -2.73764670... | [8.123058319091797, 4.448826789855957] |
7ee27425-0ed4-4621-ba26-0b11f1c1fe21 | revisiting-the-centroid-based-method-a-strong-1 | null | null | https://aclanthology.org/W17-4511 | https://aclanthology.org/W17-4511.pdf | Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization | The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we apply this ranking to possible summaries instead of sentences and use a simple greedy algorithm to find the best summary. Furthermore, we sh... | ['Gholipour Ghal', 'Demian ari'] | 2017-09-01 | null | null | null | ws-2017-9 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 3.63329947e-01 2.04783127e-01 -2.12916434e-01 -4.30141091e-01
-1.32925761e+00 -9.06946361e-01 9.23746943e-01 9.05243576e-01
-5.06579697e-01 8.34017456e-01 1.10248649e+00 -6.73266277e-02
-1.19399764e-01 -3.25585932e-01 -3.39454412e-01 -3.86991948e-01
-1.21907130e-01 6.59481943e-01 4.48996276e-01 -2.89436817... | [12.516295433044434, 9.52125072479248] |
27a36bca-f9c2-4ce5-b8af-1f3ea4979b40 | learning-edge-preserved-image-stitching-from | 2012.06194 | null | https://arxiv.org/abs/2012.06194v1 | https://arxiv.org/pdf/2012.06194v1.pdf | Learning Edge-Preserved Image Stitching from Large-Baseline Deep Homography | Image stitching is a classical and crucial technique in computer vision, which aims to generate the image with a wide field of view. The traditional methods heavily depend on the feature detection and require that scene features be dense and evenly distributed in the image, leading to varying ghosting effects and poor ... | ['Yao Zhao', 'Kang Liao', 'Chunyu Lin', 'Lang Nie'] | 2020-12-11 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 3.08567435e-01 -3.32497448e-01 5.61286882e-02 8.64999518e-02
-3.50706637e-01 -4.45336133e-01 5.37264884e-01 -7.36120462e-01
-9.68606621e-02 3.28612298e-01 7.36567229e-02 1.47183970e-01
7.99925178e-02 -6.62362576e-01 -8.53325009e-01 -1.10457611e+00
5.50566494e-01 2.34695598e-01 3.12378466e-01 -2.83943176... | [9.28562068939209, -2.352212905883789] |
d4b491f4-3ba5-488a-a372-e501ab401b9a | tsdae-using-transformer-based-sequential | 2104.06979 | null | https://arxiv.org/abs/2104.06979v3 | https://arxiv.org/pdf/2104.06979v3.pdf | TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning | Learning sentence embeddings often requires a large amount of labeled data. However, for most tasks and domains, labeled data is seldom available and creating it is expensive. In this work, we present a new state-of-the-art unsupervised method based on pre-trained Transformers and Sequential Denoising Auto-Encoder (TSD... | ['Iryna Gurevych', 'Nils Reimers', 'Kexin Wang'] | 2021-04-14 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 1.34714425e-01 2.15744041e-02 -1.66666284e-01 -5.03599048e-01
-8.19879055e-01 -4.32094276e-01 7.48645067e-01 6.80283248e-01
-7.34881639e-01 7.23577559e-01 4.17332143e-01 -8.35420713e-02
6.91472813e-02 -6.32918000e-01 -5.17795742e-01 -3.31912756e-01
2.46827811e-01 6.60209596e-01 3.17674220e-01 -4.49708372... | [10.582518577575684, 8.772275924682617] |
e4309687-5cbb-4a7c-822a-c18c478e58f0 | lsmi-sinkhorn-semi-supervised-squared-loss | 1909.02373 | null | https://arxiv.org/abs/1909.02373v3 | https://arxiv.org/pdf/1909.02373v3.pdf | LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport | Estimating mutual information is an important statistics and machine learning problem. To estimate the mutual information from data, a common practice is preparing a set of paired samples $\{(\mathbf{x}_i,\mathbf{y}_i)\}_{i=1}^n \stackrel{\mathrm{i.i.d.}}{\sim} p(\mathbf{x},\mathbf{y})$. However, in many situations, it... | ['Yao-Hung Hubert Tsai', 'Yanbin Liu', 'Makoto Yamada', 'Yi Yang', 'Ruslan Salakhutdinov', 'Tam Le'] | 2019-09-05 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.60443801e-01 -1.75950006e-01 -4.59227450e-02 -6.06471956e-01
-1.37852585e+00 -4.07430977e-01 1.15336597e-01 4.15518740e-03
-4.54673380e-01 1.06791937e+00 -3.17431688e-01 -1.85634524e-01
-4.82780993e-01 -6.59835637e-01 -8.63623559e-01 -1.01365936e+00
-2.48684082e-02 4.80553865e-01 -1.45052612e-01 3.56160969... | [9.245649337768555, 3.800542116165161] |
18b66f67-c6c9-4c32-8618-39c0588d4552 | measuring-bias-in-ai-models-with-application | 2304.13680 | null | https://arxiv.org/abs/2304.13680v2 | https://arxiv.org/pdf/2304.13680v2.pdf | Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma | The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and... | ['Javier Ortega-Garcia', 'Julian Fierrez', 'Aythami Morales', 'Ignacio Serna', 'Daniel DeAlcala'] | 2023-04-26 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 4.46758151e-01 5.07399619e-01 -3.31232995e-01 -6.48430645e-01
-2.81770945e-01 -1.97890937e-01 9.81899202e-01 2.98450738e-01
-6.03643119e-01 8.27308714e-01 1.95308641e-01 -4.41428751e-01
-6.36538744e-01 -9.66520727e-01 -4.31164414e-01 -5.98111987e-01
2.01260641e-01 5.11204958e-01 -8.28914195e-02 -6.20276779... | [8.826452255249023, 4.900684356689453] |
a75427e9-715d-47c1-9004-0073409f7abf | a-grounded-unsupervised-universal-part-of | 1904.05426 | null | http://arxiv.org/abs/1904.05426v1 | http://arxiv.org/pdf/1904.05426v1.pdf | A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages | Unsupervised part of speech (POS) tagging is often framed as a clustering
problem, but practical taggers need to \textit{ground} their clusters as well.
Grounding generally requires reference labeled data, a luxury a low-resource
language might not have. In this work, we describe an approach for low-resource
unsupervis... | ['Ying Lin', 'Heng Ji', 'Ronald Cardenas', 'Jonathan May'] | 2019-04-10 | a-grounded-unsupervised-universal-part-of-1 | https://aclanthology.org/N19-1252 | https://aclanthology.org/N19-1252.pdf | naacl-2019-6 | ['decipherment'] | ['natural-language-processing'] | [-2.14926094e-01 3.40282440e-01 -1.44775882e-01 -4.27805185e-01
-1.33418620e+00 -1.16089535e+00 3.97658050e-01 2.39334688e-01
-5.03907025e-01 7.95204699e-01 3.13842922e-01 -8.08338821e-01
2.99875945e-01 -4.25393581e-01 -4.73648638e-01 -5.74513137e-01
-7.02423975e-02 8.05818975e-01 4.89126682e-01 -3.34721953... | [10.36866569519043, 9.927753448486328] |
1793d958-17c6-41e5-89b2-465e634419d3 | dialogpt-large-scale-generative-pre-training-1 | null | null | https://aclanthology.org/2020.acl-demos.30 | https://aclanthology.org/2020.acl-demos.30.pdf | DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation | We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to at... | ['Yen-Chun Chen', 'Xiang Gao', 'Chris Brockett', 'Yizhe Zhang', 'Jingjing Liu', 'Jianfeng Gao', 'Siqi Sun', 'Michel Galley', 'Bill Dolan'] | 2020-07-01 | null | null | null | acl-2020-6 | ['conversational-response-generation'] | ['natural-language-processing'] | [ 2.91129529e-01 6.93006516e-01 4.58611213e-02 -7.45345950e-01
-1.20483184e+00 -9.00204718e-01 1.15469193e+00 -3.17532033e-01
-1.49687812e-01 1.24689209e+00 1.02182913e+00 -3.47370058e-01
5.00485718e-01 -6.25597537e-01 -1.97869927e-01 -1.26686454e-01
2.65579551e-01 1.16419291e+00 -2.35700428e-01 -1.00033021... | [12.687116622924805, 8.200798988342285] |
29e31666-d353-40a3-9b44-df859fe04e9a | document-ranking-with-a-pretrained-sequence | 2003.06713 | null | https://arxiv.org/abs/2003.06713v1 | https://arxiv.org/pdf/2003.06713v1.pdf | Document Ranking with a Pretrained Sequence-to-Sequence Model | This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted classification-based formulation of ranking, based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-t... | ['Rodrigo Nogueira', 'Zhiying Jiang', 'Jimmy Lin'] | 2020-03-14 | null | https://aclanthology.org/2020.findings-emnlp.63 | https://aclanthology.org/2020.findings-emnlp.63.pdf | findings-of-the-association-for-computational | ['ad-hoc-information-retrieval', 'passage-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.41286850e-01 -8.56159851e-02 -3.49137723e-01 -6.04216456e-01
-1.75444603e+00 -8.22462976e-01 1.45037413e+00 2.22963884e-01
-7.18245029e-01 8.43627274e-01 7.99310565e-01 -3.90526026e-01
-2.76524931e-01 -4.68868166e-01 -7.89803624e-01 -4.21730280e-01
-1.89916998e-01 8.60143483e-01 4.67253476e-01 -8.02002549... | [11.46367073059082, 7.7584099769592285] |
503a8277-36b4-4b79-b615-e3a0b7543e7c | variational-relational-point-completion-1 | 2304.09131 | null | https://arxiv.org/abs/2304.09131v1 | https://arxiv.org/pdf/2304.09131v1.pdf | Variational Relational Point Completion Network for Robust 3D Classification | Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete... | ['Ziwei Liu', 'Shuai Yi', 'Haiyu Zhao', 'Junzhe Zhang', 'Zhongang Cai', 'Xinyi Chen', 'Liang Pan'] | 2023-04-18 | null | null | null | null | ['point-cloud-completion', '3d-classification'] | ['computer-vision', 'computer-vision'] | [-2.02265695e-01 3.75304297e-02 -1.08945638e-01 -1.99978799e-01
-1.09926462e+00 -5.76152921e-01 6.63685262e-01 -2.56467730e-01
1.85023800e-01 1.14920035e-01 -5.61737567e-02 5.46856448e-02
-1.37257978e-01 -9.75106478e-01 -1.21672618e+00 -4.90961254e-01
4.09016758e-01 1.11599982e+00 1.95746168e-01 -4.28710915... | [8.39339542388916, -3.5358898639678955] |
4334556a-ebe3-40f9-b1fe-a75af9ce6493 | neural-text-generation-from-structured-data | 1603.07771 | null | http://arxiv.org/abs/1603.07771v3 | http://arxiv.org/pdf/1603.07771v3.pdf | Neural Text Generation from Structured Data with Application to the Biography Domain | This paper introduces a neural model for concept-to-text generation that
scales to large, rich domains. We experiment with a new dataset of biographies
from Wikipedia that is an order of magnitude larger than existing resources
with over 700k samples. The dataset is also vastly more diverse with a 400k
vocabulary, comp... | ['Michael Auli', 'David Grangier', 'Remi Lebret'] | 2016-03-24 | neural-text-generation-from-structured-data-1 | https://aclanthology.org/D16-1128 | https://aclanthology.org/D16-1128.pdf | emnlp-2016-11 | ['table-to-text-generation', 'concept-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.30649397e-01 5.48144162e-01 -2.44130626e-01 -3.07119697e-01
-1.20793462e+00 -6.15632534e-01 1.25549972e+00 -2.70976359e-03
-4.60381448e-01 1.47952175e+00 8.06713402e-01 -2.91867673e-01
2.68471658e-01 -1.29606128e+00 -9.27316606e-01 -2.04375833e-01
1.61803484e-01 1.06222105e+00 -7.72841796e-02 -7.63440609... | [11.649471282958984, 9.019083023071289] |
2a98e961-d4d5-4beb-bda9-b21dd10d9da6 | efficient-palm-line-segmentation-with-u-net | 2102.12127 | null | https://arxiv.org/abs/2102.12127v1 | https://arxiv.org/pdf/2102.12127v1.pdf | Efficient Palm-Line Segmentation with U-Net Context Fusion Module | Many cultures around the world believe that palm reading can be used to predict the future life of a person. Palmistry uses features of the hand such as palm lines, hand shape, or fingertip position. However, the research on palm-line detection is still scarce, many of them applied traditional image processing techniqu... | ['Ta Minh Thanh', 'Ngoc N. Tran', 'Linh Bao Doan', 'Son Trung Nguyen', 'Toan Pham Van'] | 2021-02-24 | null | null | null | null | ['unet-segmentation', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 2.02206179e-01 -4.80648965e-01 -1.11760475e-01 -3.06274652e-01
-6.91062585e-02 -5.85130572e-01 2.36178428e-01 -5.13955891e-01
-4.12947208e-01 4.59060848e-01 -2.59851068e-01 -4.72779348e-02
6.83174729e-02 -7.66989768e-01 -4.66541439e-01 -5.58534026e-01
3.89184713e-01 9.81076583e-02 1.83762491e-01 5.99383861... | [6.5646185874938965, -0.5085762143135071] |
8b1a3500-9760-4067-a460-23add902f8f2 | dcso-dynamic-combination-of-detector-scores | 1911.10418 | null | https://arxiv.org/abs/1911.10418v1 | https://arxiv.org/pdf/1911.10418v1.pdf | DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles | Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth. In this paper, an unsupervised outlier detector combination framework called DCSO is proposed, demonstrated and assessed for the dynamic selection of most compete... | ['Yue Zhao', 'Maciej K. Hryniewicki'] | 2019-11-23 | null | null | null | null | ['outlier-ensembles'] | ['methodology'] | [-1.92640662e-01 -3.43548328e-01 1.12065189e-01 -3.02676912e-02
-7.55421221e-01 -4.50798452e-01 3.71309131e-01 9.48447645e-01
-1.58444226e-01 4.04083967e-01 5.00412099e-02 -9.60924104e-02
-8.85239661e-01 -4.27041471e-01 -1.22130863e-01 -7.78649211e-01
-5.72195530e-01 4.47753996e-01 3.55588347e-01 1.61322936... | [7.543262481689453, 2.736804962158203] |
a78b2376-d5da-4b25-be50-785389bd0b41 | bora-bayesian-optimization-for-resource | 2210.05977 | null | https://arxiv.org/abs/2210.05977v1 | https://arxiv.org/pdf/2210.05977v1.pdf | BORA: Bayesian Optimization for Resource Allocation | Optimal resource allocation is gaining a renewed interest due its relevance as a core problem in managing, over time, cloud and high-performance computing facilities. Semi-Bandit Feedback (SBF) is the reference method for efficiently solving this problem. In this paper we propose (i) an extension of the optimal resourc... | ['Francesco Archetti', 'Andrea Ponti', 'Antonio Candelieri'] | 2022-10-12 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 6.81639463e-02 -4.38657939e-01 -6.92479312e-01 -2.26213470e-01
-9.36111987e-01 -4.03032303e-01 5.04490197e-01 1.26874939e-01
-6.62419081e-01 1.08509612e+00 2.03402176e-01 -8.03190529e-01
-1.00824773e+00 -5.12905896e-01 -4.71181005e-01 -8.59034538e-01
-3.95258293e-02 7.90818810e-01 -1.04486659e-01 1.62539899... | [4.55596923828125, 3.2507448196411133] |
a9e411fa-17d8-4b28-8dca-51de0d05f542 | estimating-the-amenibility-of-new-domains-for | null | null | https://aclanthology.org/W16-0804 | https://aclanthology.org/W16-0804.pdf | Estimating the amenibility of new domains for deception detection | null | ['Eileen Fitzpatrick', 'Joan Bachenko'] | 2016-06-01 | null | null | null | ws-2016-6 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.357299327850342, 3.717125654220581] |
0eb8fb1d-7355-4ed8-9d9c-d14b4ee1fed6 | exploiting-simulated-user-feedback-for | 2304.13874 | null | https://arxiv.org/abs/2304.13874v3 | https://arxiv.org/pdf/2304.13874v3.pdf | Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond | This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS) systems. While CS systems enjoy profuse advancements across multiple aspects, recent research fails to successfully incorporate feedback from the users. One of the main reasons for that is the lack o... | ['Fabio Crestani', 'Jeffrey Dalton', 'Mohammad Aliannejadi', 'Ivan Sekulić', 'Paul Owoicho'] | 2023-04-26 | null | null | null | null | ['passage-retrieval', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.11223586e-01 -9.68805626e-02 -1.05835781e-01 -3.60188931e-01
-1.42877126e+00 -8.61473739e-01 8.16579282e-01 3.58690917e-01
-6.01484537e-01 5.95539212e-01 6.46264672e-01 -6.90959632e-01
-1.24747343e-01 -1.60805464e-01 -2.46603459e-01 -8.27276036e-02
1.40466109e-01 5.45980155e-01 3.70442837e-01 -9.40524936... | [12.12612247467041, 7.818840980529785] |
d9cc975f-f034-44e7-91ff-b0443e51b277 | knowledge-adaptation-teaching-to-adapt | 1702.02052 | null | http://arxiv.org/abs/1702.02052v1 | http://arxiv.org/pdf/1702.02052v1.pdf | Knowledge Adaptation: Teaching to Adapt | Domain adaptation is crucial in many real-world applications where the
distribution of the training data differs from the distribution of the test
data. Previous Deep Learning-based approaches to domain adaptation need to be
trained jointly on source and target domain data and are therefore unappealing
in scenarios whe... | ['John G. Breslin', 'Sebastian Ruder', 'Parsa Ghaffari'] | 2017-02-07 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 4.22852626e-03 1.03550762e-01 -2.39641294e-01 -5.19533992e-01
-7.31925070e-01 -1.14213145e+00 8.14538598e-01 5.10103166e-01
-6.43621445e-01 8.59859765e-01 -5.84175214e-02 -4.20452327e-01
-1.82844535e-01 -7.17038453e-01 -8.08257878e-01 -4.61746275e-01
4.50680226e-01 8.13208878e-01 6.95684612e-01 -6.32592738... | [10.80500602722168, 7.6890435218811035] |
1885f1c7-9b41-48ba-bde5-969acbd6ded5 | phone-based-keyword-spotting-for-transcribing | null | null | https://aclanthology.org/2021.alta-1.8 | https://aclanthology.org/2021.alta-1.8.pdf | Phone Based Keyword Spotting for Transcribing Very Low Resource Languages | We investigate the efficiency of two very different spoken term detection approaches for transcription when the available data is insufficient to train a robust speech recognition system. This work is grounded in a very low-resource language documentation scenario where only a few minutes of recording have been transcr... | ['Laurent Besacier', 'Steven Bird', 'Eric Le Ferrand'] | null | null | null | null | alta-2021-12 | ['robust-speech-recognition', 'keyword-spotting'] | ['speech', 'speech'] | [ 3.29545796e-01 8.70585740e-02 -1.00575007e-01 -4.56606567e-01
-1.33699322e+00 -8.56218934e-01 9.38877404e-01 -3.89206856e-02
-5.98382831e-01 4.94532645e-01 3.19597691e-01 -5.43042600e-01
1.29814282e-01 -1.91893399e-01 -5.65968081e-02 -6.14409149e-01
-5.25691845e-02 7.75862575e-01 2.89884984e-01 -5.31955719... | [14.370945930480957, 6.711470603942871] |
51fa4293-c254-497b-8888-c366181f8242 | visual-affordance-and-function-understanding | 1807.06775 | null | http://arxiv.org/abs/1807.06775v1 | http://arxiv.org/pdf/1807.06775v1.pdf | Visual Affordance and Function Understanding: A Survey | Nowadays, robots are dominating the manufacturing, entertainment and
healthcare industries. Robot vision aims to equip robots with the ability to
discover information, understand it and interact with the environment. These
capabilities require an agent to effectively understand object affordances and
functionalities in... | ['Mohammed Hassanin', 'Salman Khan', 'Murat Tahtali'] | 2018-07-18 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 2.68777370e-01 2.10067660e-01 -5.65816045e-01 -2.90839970e-01
3.36960196e-01 -7.58719921e-01 3.84826213e-01 4.16914344e-01
-1.83234252e-02 4.26392913e-01 4.88155931e-02 8.83637145e-02
-5.16526699e-01 -3.37775677e-01 -6.42935395e-01 -5.27209342e-01
-3.91075671e-01 3.92034531e-01 7.31914714e-02 -3.58813167... | [5.154612064361572, -0.09053874015808105] |
e9221826-c4e0-4d9f-a6b4-809aa0147702 | pp-matting-high-accuracy-natural-image | 2204.09433 | null | https://arxiv.org/abs/2204.09433v1 | https://arxiv.org/pdf/2204.09433v1.pdf | PP-Matting: High-Accuracy Natural Image Matting | Natural image matting is a fundamental and challenging computer vision task. It has many applications in image editing and composition. Recently, deep learning-based approaches have achieved great improvements in image matting. However, most of them require a user-supplied trimap as an auxiliary input, which limits the... | ['dianhai yu', 'Xiaoguang Hu', 'Qingqing Dang', 'Yuning Du', 'Zhiliang Yu', 'Zeyu Chen', 'Zewu Wu', 'Shiyu Tang', 'Lutao Chu', 'Yuying Hao', 'Juncai Peng', 'Jian Wang', 'Yi Liu', 'Guowei Chen'] | 2022-04-20 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.61635387e-01 -1.39954343e-01 -2.85371877e-02 -3.28448921e-01
-6.58629358e-01 -1.60400093e-01 5.39215744e-01 -4.01475847e-01
-1.46528482e-01 4.10715163e-01 1.20351296e-02 -3.19124818e-01
3.19138497e-01 -7.96027720e-01 -9.18631256e-01 -8.24469864e-01
5.53459585e-01 2.82411456e-01 5.02048790e-01 -1.99643642... | [10.642117500305176, -0.8910134434700012] |
b5ad31c1-429c-4713-84ac-02edc0067011 | dynamic-graph-cnn-for-learning-on-point | 1801.07829 | null | https://arxiv.org/abs/1801.07829v2 | https://arxiv.org/pdf/1801.07829v2.pdf | Dynamic Graph CNN for Learning on Point Clouds | Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success ... | ['Yue Wang', 'Yongbin Sun', 'Michael M. Bronstein', 'Ziwei Liu', 'Sanjay E. Sarma', 'Justin M. Solomon'] | 2018-01-24 | null | null | null | null | ['3d-part-segmentation', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-3.93267006e-01 8.84310305e-02 -9.59487781e-02 -5.18847704e-01
-8.47654268e-02 -6.58973873e-01 7.62207568e-01 3.10281575e-01
-2.64371544e-01 -1.56866223e-01 -1.46481007e-01 -5.17370105e-01
-1.20245606e-01 -1.12343955e+00 -9.11138892e-01 -1.27783656e-01
-2.58034080e-01 7.86086738e-01 4.44145858e-01 -3.62463087... | [7.921504020690918, -3.717244863510132] |
b30ab5a4-dc71-49c7-9b05-712ae8648c22 | video-prediction-by-efficient-transformers | 2212.06026 | null | https://arxiv.org/abs/2212.06026v1 | https://arxiv.org/pdf/2212.06026v1.pdf | Video Prediction by Efficient Transformers | Video prediction is a challenging computer vision task that has a wide range of applications. In this work, we present a new family of Transformer-based models for video prediction. Firstly, an efficient local spatial-temporal separation attention mechanism is proposed to reduce the complexity of standard Transformers.... | ['Guillaume-Alexandre Bilodeau', 'Xi Ye'] | 2022-12-12 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 7.44571164e-02 -1.05787523e-01 -2.04879731e-01 -2.22001076e-01
-5.87012231e-01 1.21331476e-01 5.82626283e-01 -4.72259521e-01
-2.46833339e-01 3.94471079e-01 3.22646230e-01 -2.40993947e-01
1.59699455e-01 -5.42999566e-01 -9.29452419e-01 -7.50518501e-01
2.18447834e-01 5.46406284e-02 7.32003868e-01 5.52857481... | [8.900785446166992, 0.2868964672088623] |
68ac4983-1ce9-4437-84fc-dd20988170cb | improving-scientific-relation-classification | null | null | https://aclanthology.org/Y18-1015 | https://aclanthology.org/Y18-1015.pdf | Improving Scientific Relation Classification with Task Specific Supersense | null | ['Kentaro Inui', 'Paul Reisert', 'Naoya Inoue', 'Qin Dai'] | null | null | null | null | paclic-2018-12 | ['relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.225683212280273, 3.765061855316162] |
c66d46df-8af5-431f-84a6-b8f0d1ea1b5c | mug-a-general-meeting-understanding-and | 2303.13939 | null | https://arxiv.org/abs/2303.13939v2 | https://arxiv.org/pdf/2303.13939v2.pdf | MUG: A General Meeting Understanding and Generation Benchmark | Listening to long video/audio recordings from video conferencing and online courses for acquiring information is extremely inefficient. Even after ASR systems transcribe recordings into long-form spoken language documents, reading ASR transcripts only partly speeds up seeking information. It has been observed that a ra... | ['Zhou Zhao', 'Yi Ren', 'Jinglin Liu', 'Zhijie Yan', 'Wen Wang', 'Qian Chen', 'Hai Yu', 'Jiaqing Liu', 'Chong Deng', 'Qinglin Zhang'] | 2023-03-24 | null | null | null | null | ['topic-coverage', 'extractive-summarization', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.22670746e-01 4.14133817e-01 -1.30720139e-01 -2.57791370e-01
-1.92035615e+00 -8.94051373e-01 6.85508728e-01 4.09891725e-01
-2.65012264e-01 6.63025677e-01 7.35430419e-01 -2.08052173e-01
1.94000974e-02 -3.87757346e-02 -5.01304924e-01 -3.28363329e-01
-3.75273041e-02 5.61309576e-01 2.40111828e-01 -2.29293242... | [12.610821723937988, 9.392475128173828] |
2426c2c3-5dde-4ad9-ae9f-0126c324911f | rethinking-the-evaluation-for-conversational | 2305.13112 | null | https://arxiv.org/abs/2305.13112v1 | https://arxiv.org/pdf/2305.13112v1.pdf | Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models | The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendat... | ['Ji-Rong Wen', 'Jingyuan Wang', 'Wayne Xin Zhao', 'Xinyu Tang', 'Xiaolei Wang'] | 2023-05-22 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [-1.56954855e-01 5.27859151e-01 -3.68444137e-02 -3.90172452e-01
-5.83937705e-01 -5.89192629e-01 8.23393583e-01 -1.74772292e-01
9.34929848e-02 6.98337436e-01 6.08730316e-01 -7.55868256e-01
-2.31091216e-01 -5.87177336e-01 -3.80722076e-01 -1.57509923e-01
-3.15125845e-02 4.78769332e-01 -1.07109509e-01 -6.56842232... | [12.267576217651367, 7.533604145050049] |
3cff48d3-641f-43db-b30a-048ee9829ed0 | obstacle-transformer-a-trajectory-prediction | 2304.07711 | null | https://arxiv.org/abs/2304.07711v1 | https://arxiv.org/pdf/2304.07711v1.pdf | Obstacle-Transformer: A Trajectory Prediction Network Based on Surrounding Trajectories | Recurrent Neural Network, Long Short-Term Memory, and Transformer have made great progress in predicting the trajectories of moving objects. Although the trajectory element with the surrounding scene features has been merged to improve performance, there still exist some problems to be solved. One is that the time seri... | ['ChengWei Wu', 'Quanqi Zhang', 'Qingjie Chai', 'Wendong Zhang'] | 2023-04-16 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-8.00419748e-02 -6.78597331e-01 -2.11883914e-02 -4.25846905e-01
-6.05503470e-02 -1.16790220e-01 5.12398362e-01 -1.72186688e-01
-5.12768388e-01 4.72195894e-01 -1.15025928e-02 -1.77724034e-01
-7.59158507e-02 -8.81521881e-01 -6.18798852e-01 -6.92849100e-01
-2.29982242e-01 4.03301306e-02 7.24698067e-01 -1.55740663... | [6.322977542877197, 1.0382344722747803] |
47a421e9-ab07-46d9-9f7a-f6fbd091f93b | sentinel-2-sharpening-using-a-single | null | null | https://ieeexplore.ieee.org/document/9464640 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9464640 | Sentinel-2 Sharpening Using a Single Unsupervised Convolutional Neural Network With MTF-Based Degradation Model | The Sentinel-2 (S2) constellation provides multispectral images at 10 m, 20 m, and 60 m resolution bands. Obtaining all bands at 10 m resolution would benefit many applications. Recently, many model-based and deep learning (DL)-based sharpening methods have been proposed. However, the downside of those methods is that ... | ['Han V. Nguyen; Magnus O. Ulfarsson; Johannes R. Sveinsson; Mauro Dalla Mura'] | 2021-06-24 | null | null | null | ieee-journal-of-selected-topics-in-applied-7 | ['pansharpening'] | ['computer-vision'] | [ 4.64880794e-01 -2.11312532e-01 -1.29841464e-02 -3.51126701e-01
-1.17797256e+00 -3.83375645e-01 3.09701979e-01 -3.71630669e-01
-5.09980679e-01 6.89371169e-01 3.52456234e-03 -1.17985345e-01
-4.75052655e-01 -9.99318779e-01 -5.90359390e-01 -1.11203897e+00
-3.54741849e-02 -3.20682049e-01 2.75319248e-01 -4.88549381... | [10.193309783935547, -1.9103509187698364] |
517612e6-2af5-49a6-a9cf-7650cdcbfbe8 | extended-local-binary-patterns-for-efficient | 1907.09160 | null | https://arxiv.org/abs/1907.09160v2 | https://arxiv.org/pdf/1907.09160v2.pdf | Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition | Facial Micro-Expressions (MEs) are spontaneous, involuntary facial movements when a person experiences an emotion but deliberately or unconsciously attempts to conceal his or her genuine emotions. Recently, ME recognition has attracted increasing attention due to its potential applications such as clinical diagnosis, b... | ['Matti Pietikäinen', 'Zhong Liu', 'Chengyu Guo', 'Li Liu', 'Jingyun Liang', 'Geng Zhan'] | 2019-07-22 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 8.99922401e-02 -4.76687759e-01 -3.46989483e-01 -3.52202624e-01
-5.48736453e-01 -2.78401375e-02 5.49584389e-01 -4.62144732e-01
-3.12956989e-01 4.27162439e-01 -2.04354767e-02 2.54198581e-01
-1.95882678e-01 -4.19621974e-01 -3.41697127e-01 -1.29731250e+00
-2.70243734e-01 -1.34791031e-01 -1.29531279e-01 -2.24991292... | [13.633965492248535, 1.7720701694488525] |
ca6a21f6-0211-4256-bd85-0d66b5f78d33 | sketchmate-deep-hashing-for-million-scale | 1804.01401 | null | http://arxiv.org/abs/1804.01401v1 | http://arxiv.org/pdf/1804.01401v1.pdf | SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval | We propose a deep hashing framework for sketch retrieval that, for the first
time, works on a multi-million scale human sketch dataset. Leveraging on this
large dataset, we explore a few sketch-specific traits that were otherwise
under-studied in prior literature. Instead of following the conventional sketch
recognitio... | ['Yi-Zhe Song', 'Kaiyue Pang', 'Zhanyu Ma', 'Tongtong Yuan', 'Timothy M. Hospedales', 'Tao Xiang', 'Peng Xu', 'Yongye Huang', 'Jun Guo'] | 2018-04-04 | sketchmate-deep-hashing-for-million-scale-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Xu_SketchMate_Deep_Hashing_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_SketchMate_Deep_Hashing_CVPR_2018_paper.pdf | cvpr-2018-6 | ['sketch-recognition'] | ['computer-vision'] | [-1.06161386e-01 -5.40782392e-01 -3.97787124e-01 -1.56581059e-01
-7.16000736e-01 -6.04872465e-01 6.64374113e-01 -9.84539315e-02
-2.27248460e-01 2.62044102e-01 2.72981733e-01 -1.05485678e-01
-7.18990192e-02 -7.70241797e-01 -5.87163627e-01 -5.29851735e-01
-2.71944612e-01 4.80331481e-01 1.63800225e-01 -3.81999761... | [11.69491958618164, 0.5570034980773926] |
c71eefbb-2eea-4d0d-b0c8-cec0081cd323 | class-aware-contrastive-semi-supervised | 2203.02261 | null | https://arxiv.org/abs/2203.02261v3 | https://arxiv.org/pdf/2203.02261v3.pdf | Class-Aware Contrastive Semi-Supervised Learning | Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization. However, its training procedure suffers from confirmation bias due to the noise contained in self-generated artificial labels. Moreover, the model's judgment becomes noisier in real-world applications with extensive ou... | ['Long Zeng', 'Chengjie Wang', 'Wei zhang', 'Feng Zheng', 'Yong liu', 'Guannan Jiang', 'Shuyi Zhang', 'Kai Wu', 'Fan Yang'] | 2022-03-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Class-Aware_Contrastive_Semi-Supervised_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Class-Aware_Contrastive_Semi-Supervised_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 2.59283572e-01 -1.04598656e-01 -4.26843524e-01 -8.43680322e-01
-1.41630912e+00 -6.35645866e-01 5.05397975e-01 1.71705902e-01
-5.06505907e-01 9.34592128e-01 -2.09130347e-01 -1.77278608e-01
-7.05348849e-02 -3.22943479e-01 -6.60626292e-01 -1.14908278e+00
2.59404689e-01 3.82985830e-01 1.73294097e-01 1.90229580... | [9.42251205444336, 3.8769142627716064] |
23899834-e6e5-4717-9264-16302535abfe | tt-nf-tensor-train-neural-fields | 2209.15529 | null | https://arxiv.org/abs/2209.15529v1 | https://arxiv.org/pdf/2209.15529v1.pdf | TT-NF: Tensor Train Neural Fields | Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we introduce a novel low-rank representation termed Tensor Train Neural Fields (TT-NF) for learning neural fields on dense regular grids and ef... | ['Luc van Gool', 'Konrad Schindler', 'Christos Sakaridis', 'Mikhail Usvyatsov', 'Anton Obukhov'] | 2022-09-30 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [ 2.29391083e-01 -5.51290512e-02 1.30003422e-01 -3.63041401e-01
-9.09310400e-01 -2.84000486e-01 2.98087209e-01 -2.44486444e-02
-3.47924471e-01 6.06042087e-01 4.39454913e-01 -3.77931111e-02
-8.37947607e-01 -7.10914552e-01 -1.09324014e+00 -9.18798089e-01
-4.01148468e-01 7.00068250e-02 -3.30377400e-01 -2.03746587... | [11.463295936584473, -2.1143999099731445] |
8e034986-4092-4897-a5d6-8a2a9f3db048 | relwalk-a-latent-variable-model-approach-to-2 | null | null | https://aclanthology.org/2021.eacl-main.133 | https://aclanthology.org/2021.eacl-main.133.pdf | RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding | Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities. Although progresses have been achieved, existing methods are heuristically motivated and theoretical understanding of such embeddings is comparatively underdeve... | ['Ken-ichi Kawarabayashi', 'Yuichi Yoshida', 'Huda Hakami', 'Danushka Bollegala'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.10290535e-01 8.37892830e-01 -4.83672112e-01 -3.57740581e-01
-1.99252859e-01 -4.19159949e-01 6.09501243e-01 4.59161162e-01
-4.33886588e-01 7.13733673e-01 2.20856965e-01 -1.89938396e-01
-7.99547911e-01 -1.20477426e+00 -7.90492892e-01 -5.29631495e-01
-7.52828360e-01 5.05518079e-01 2.74319828e-01 -1.97959063... | [8.748235702514648, 7.782365798950195] |
26ccb2e1-01e4-4698-ab99-345c21c3f784 | memory-efficient-tries-for-sequential-pattern | 2202.06834 | null | https://arxiv.org/abs/2202.06834v1 | https://arxiv.org/pdf/2202.06834v1.pdf | Memory Efficient Tries for Sequential Pattern Mining | The rapid and continuous growth of data has increased the need for scalable mining algorithms in unsupervised learning and knowledge discovery. In this paper, we focus on Sequential Pattern Mining (SPM), a fundamental topic in knowledge discovery that faces a well-known memory bottleneck. We examine generic dataset mod... | ['Andre A. Cire', 'Willem-Jan van Hoeve', 'Amin Hosseininasab'] | 2022-02-06 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 3.45006824e-01 -1.31082803e-01 -5.41465938e-01 -2.15144888e-01
-8.13278556e-02 -3.29527020e-01 1.18477575e-01 5.03347397e-01
-2.99984425e-01 9.12605643e-01 -3.67907405e-01 -5.62091589e-01
-8.13273430e-01 -1.13199413e+00 -4.04669553e-01 -3.47718269e-01
-4.39595014e-01 9.85440135e-01 3.17142725e-01 1.88166380... | [8.304780006408691, 6.278511047363281] |
6439de6a-27f5-4bf7-adac-663742200371 | sketch-guided-object-localization-in-natural | 2008.06551 | null | https://arxiv.org/abs/2008.06551v1 | https://arxiv.org/pdf/2008.06551v1.pdf | Sketch-Guided Object Localization in Natural Images | We introduce the novel problem of localizing all the instances of an object (seen or unseen during training) in a natural image via sketch query. We refer to this problem as sketch-guided object localization. This problem is distinctively different from the traditional sketch-based image retrieval task where the galler... | ['Anirban Chakraborty', 'Aditay Tripathi', 'Rajath R Dani', 'Anand Mishra'] | 2020-08-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6490_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510528.pdf | eccv-2020-8 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 4.21815738e-02 -3.68892729e-01 -2.50974417e-01 -2.80093759e-01
-1.36295283e+00 -8.66101921e-01 9.52055275e-01 2.94961594e-02
-3.76490980e-01 3.44500214e-01 -7.72930011e-02 2.31736884e-01
1.61460899e-02 -5.26593745e-01 -9.48417246e-01 -6.78007960e-01
2.67961413e-01 6.72031164e-01 5.35541892e-01 -8.75302702... | [11.648612022399902, 0.6234824061393738] |
83360a1f-1d71-4019-ae56-acac90a38986 | capturing-humans-in-motion-temporal-attentive | 2203.08534 | null | https://arxiv.org/abs/2203.08534v1 | https://arxiv.org/pdf/2203.08534v1.pdf | Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation from Monocular Video | Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local context relations of human motion. To address this... | ['Hong-Yuan Mark Liao', 'Tyng-Luh Liu', 'Jen-Chun Lin', 'Wen-Li Wei'] | 2022-03-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wei_Capturing_Humans_in_Motion_Temporal-Attentive_3D_Human_Pose_and_Shape_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wei_Capturing_Humans_in_Motion_Temporal-Attentive_3D_Human_Pose_and_Shape_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-5.25524616e-01 -5.84882736e-01 -9.61488560e-02 -2.05796152e-01
-7.58002251e-02 -2.36877650e-01 3.86130780e-01 -4.73611116e-01
-4.66097444e-01 3.66543889e-01 5.45802593e-01 2.21402600e-01
1.74812004e-01 -4.96167243e-01 -4.40097123e-01 -5.26502609e-01
-3.05517048e-01 1.74205944e-01 5.13768971e-01 -3.29241753... | [7.252886772155762, -0.5278599858283997] |
d50f3cfa-0787-4dd2-97dd-7cbe9cc66f4a | towards-automated-feature-engineering-for | 1909.01185 | null | https://arxiv.org/abs/1909.01185v1 | https://arxiv.org/pdf/1909.01185v1.pdf | Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs | Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspect... | ['Liyun He-Guelton', 'Léa Laporte', 'Pierre-Edouard Portier', 'Yvan Lucas', 'Sylvie Calabretto', 'Olivier Caelen', 'Michael Granitzer'] | 2019-09-03 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 2.80722171e-01 -2.17131317e-01 -3.40329051e-01 -4.55874681e-01
9.31130257e-03 -2.84998924e-01 6.35909379e-01 4.69911277e-01
-4.99398291e-01 6.83845520e-01 -2.62795359e-01 -5.31614900e-01
-3.22226912e-01 -1.19068241e+00 -2.99000919e-01 -5.69253445e-01
-1.96278036e-01 8.56375933e-01 3.08765769e-01 -1.65552378... | [7.992142200469971, 4.9940361976623535] |
e830f463-677b-473e-9225-bfa9750a5f83 | 3d-human-pose-shape-and-texture-from-low | 2103.06498 | null | https://arxiv.org/abs/2103.06498v1 | https://arxiv.org/pdf/2103.06498v1.pdf | 3D Human Pose, Shape and Texture from Low-Resolution Images and Videos | 3D human pose and shape estimation from monocular images has been an active research area in computer vision. Existing deep learning methods for this task rely on high-resolution input, which however, is not always available in many scenarios such as video surveillance and sports broadcasting. Two common approaches to ... | ['Fernando de la Torre', 'Laszlo A. Jeni', 'Francesc Moreno-Noguer', 'Hao Chen', 'Xiangyu Xu'] | 2021-03-11 | null | null | null | null | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [ 2.12136969e-01 -2.22357690e-01 -1.45943061e-01 -4.01135832e-01
-7.16802597e-01 -7.33680204e-02 3.99457633e-01 -2.27079064e-01
-6.68898761e-01 7.88094580e-01 1.25199020e-01 3.30568522e-01
9.57693383e-02 -7.85024285e-01 -9.82969284e-01 -6.55256689e-01
1.62716776e-01 3.78724813e-01 5.73614776e-01 -2.04545438... | [7.113180160522461, -0.958512008190155] |
d5b269c9-42a0-467d-ae92-b327b5d6cc59 | branching-model-with-state-dependent | 2306.02893 | null | https://arxiv.org/abs/2306.02893v1 | https://arxiv.org/pdf/2306.02893v1.pdf | Branching model with state dependent offspring distribution for Chlamydia spread | Chlamydiae are bacteria with an interesting unusual developmental cycle. A single bacterium in its infectious form (elementary body, EB) enters the host cell, where it converts into its dividing form (reticulate body, RB), and divides by binary fission. Since only the EB form is infectious, before the host cell dies, R... | ['Máté Szalai', 'Péter Kevei'] | 2023-06-05 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 2.50026137e-01 2.13115439e-01 4.62212786e-02 5.83265841e-01
6.97625399e-01 -6.09485805e-01 5.74135303e-01 1.45975947e-01
-5.11036992e-01 1.11775529e+00 -3.89563799e-01 -2.47757673e-01
3.00295889e-01 -9.86529529e-01 -5.33104002e-01 -1.38849998e+00
-1.53527990e-01 1.08137441e+00 3.21733087e-01 8.38504173... | [5.710201263427734, 4.243622779846191] |
215d2154-6808-42e9-b7ac-7b5e487743a9 | the-effects-of-just-in-time-delivery-on | 2212.12285 | null | https://arxiv.org/abs/2212.12285v1 | https://arxiv.org/pdf/2212.12285v1.pdf | The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis | Fooji Inc. is a social media engagement platform that has created a proprietary "Just-in-time" delivery network to provide prizes to social media marketing campaign participants in real-time. In this paper, we prove the efficacy of the "Just-in-time" delivery network through a cluster analysis that extracts and present... | ['Nathan Klarer', 'Raziel Ruíz', 'Moisés Ramírez'] | 2022-12-23 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-1.72091439e-01 1.72778189e-01 -7.13690460e-01 -4.16220397e-01
-3.80875140e-01 -6.26976430e-01 8.54099631e-01 3.84414345e-01
-3.89730811e-01 2.33007625e-01 5.61818659e-01 -8.99807096e-01
-6.27973437e-01 -1.12328291e+00 -4.49621201e-01 -1.83585331e-01
-3.40899110e-01 3.78930688e-01 -4.78245050e-01 -5.32938898... | [10.241959571838379, 6.679447650909424] |
bb8da3c2-1dc6-4a2d-b5e9-ad361ec532ed | 190513539 | 1905.13539 | null | https://arxiv.org/abs/1905.13539v4 | https://arxiv.org/pdf/1905.13539v4.pdf | Unsupervised Object Segmentation by Redrawing | Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks. Since the masks have to be provided at pixel level, building such a dataset for any new domain can be very time-consuming. We present R... | ['Thierry Artières', 'Mickaël Chen', 'Ludovic Denoyer'] | 2019-05-27 | unsupervised-object-segmentation-by-redrawing | http://papers.nips.cc/paper/9434-unsupervised-object-segmentation-by-redrawing | http://papers.nips.cc/paper/9434-unsupervised-object-segmentation-by-redrawing.pdf | neurips-2019-12 | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 8.21005762e-01 4.33059484e-01 1.85829401e-01 -2.89268643e-01
-4.81265128e-01 -1.02199316e+00 5.19936204e-01 -6.11848477e-03
-7.40437448e-01 7.27928460e-01 -6.40767217e-01 -2.78212354e-02
1.88387960e-01 -1.11249328e+00 -1.16082573e+00 -9.84384596e-01
2.75334567e-01 7.58581221e-01 8.00438225e-01 5.45985401... | [10.428079605102539, 0.028521932661533356] |
b722a599-16f6-46b1-b5bf-e0e9f9717798 | pifuhd-multi-level-pixel-aligned-implicit | 2004.00452 | null | https://arxiv.org/abs/2004.00452v1 | https://arxiv.org/pdf/2004.00452v1.pdf | PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization | Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often... | ['Jason Saragih', 'Shunsuke Saito', 'Tomas Simon', 'Hanbyul Joo'] | 2020-04-01 | pifuhd-multi-level-pixel-aligned-implicit-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Saito_PIFuHD_Multi-Level_Pixel-Aligned_Implicit_Function_for_High-Resolution_3D_Human_Digitization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Saito_PIFuHD_Multi-Level_Pixel-Aligned_Implicit_Function_for_High-Resolution_3D_Human_Digitization_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-object-reconstruction-from-a-single-image', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.80454785e-01 1.42185301e-01 1.55118689e-01 -4.51098502e-01
-9.43661690e-01 -3.60058725e-01 5.49609780e-01 7.43187219e-02
-2.23374128e-01 4.37137127e-01 4.66133118e-01 -4.87141013e-02
6.67143688e-02 -9.24464703e-01 -9.37310338e-01 -1.75534323e-01
2.02718720e-01 9.34496105e-01 2.70675719e-01 -2.04038769... | [8.770164489746094, -3.285536766052246] |
b7bd9485-4c81-41b8-8006-f9b45dec9d4f | cross-lingual-linking-of-automatically | null | null | https://aclanthology.org/2022.lrec-1.713 | https://aclanthology.org/2022.lrec-1.713.pdf | Cross-lingual Linking of Automatically Constructed Frames and FrameNet | A semantic frame is a conceptual structure describing an event, relation, or object along with its participants. Several semantic frame resources have been manually elaborated, and there has been much interest in the possibility of applying semantic frames designed for a particular language to other languages, which ha... | ['Ryohei Sasano'] | null | null | null | null | lrec-2022-6 | ['cross-lingual-word-embeddings'] | ['natural-language-processing'] | [ 1.22480266e-01 3.66861939e-01 -2.24989593e-01 -5.90876639e-01
-7.96518624e-01 -4.51751530e-01 8.05586100e-01 4.26063329e-01
-6.23894989e-01 7.56255805e-01 7.30289578e-01 -2.99447656e-01
1.24957912e-01 -7.97515750e-01 -3.84429038e-01 -2.65595704e-01
2.45140150e-01 3.12617451e-01 6.40911996e-01 -2.78671086... | [10.23812484741211, 9.296518325805664] |
b6fb260a-384c-46e6-85bd-8898c26de6a1 | discriminative-invariant-kernel-features-a | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Pal_Discriminative_Invariant_Kernel_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Pal_Discriminative_Invariant_Kernel_CVPR_2016_paper.pdf | Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation | We propose an explicitly discriminative and `simple' approach to generate invariance to nuisance transformations modeled as unitary. In practice, the approach works well to handle non-unitary transformations as well. Our theoretical results extend the reach of a recent theory of invariance to discriminative and kerneli... | ['Felix Juefei-Xu', 'Dipan K. Pal', 'Marios Savvides'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['unsupervised-face-recognition'] | ['computer-vision'] | [ 5.09778738e-01 3.81626077e-02 1.24934845e-01 -6.69160306e-01
-1.01867819e+00 -6.03064060e-01 7.78706968e-01 -5.24289608e-01
-3.83758008e-01 5.50802469e-01 1.15466081e-02 4.02105004e-02
-3.92298341e-01 -7.86761403e-01 -8.97834539e-01 -9.38522577e-01
-6.13438226e-02 8.01057577e-01 4.36321944e-02 -3.57358277... | [13.215163230895996, 0.2831770181655884] |
308f33ee-fa2b-4c00-ace5-35f78355abf0 | bi-directional-differentiable-input | 1811.01116 | null | http://arxiv.org/abs/1811.01116v2 | http://arxiv.org/pdf/1811.01116v2.pdf | Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation | We aim to better exploit the limited amounts of parallel text available in
low-resource settings by introducing a differentiable reconstruction loss for
neural machine translation (NMT). This loss compares original inputs to
reconstructed inputs, obtained by back-translating translation hypotheses into
the input langua... | ['Xing Niu', 'Weijia Xu', 'Marine Carpuat'] | 2018-11-02 | bi-directional-differentiable-input-1 | https://aclanthology.org/N19-1043 | https://aclanthology.org/N19-1043.pdf | naacl-2019-6 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 4.84854192e-01 1.86823756e-01 -7.55515277e-01 -3.32343996e-01
-1.56675887e+00 -7.30871081e-01 1.03508294e+00 -3.09961438e-01
-5.42083204e-01 1.09719741e+00 4.43532974e-01 -7.88519323e-01
4.51061487e-01 -4.75514054e-01 -1.27892327e+00 -7.66841844e-02
5.07475317e-01 9.36524689e-01 -4.22967643e-01 -6.77414089... | [11.660714149475098, 10.222047805786133] |
b81feb16-f0b3-4376-8d6f-47f42a8e2099 | associative-learning-mechanism-for-drug | 2205.15364 | null | https://arxiv.org/abs/2205.15364v4 | https://arxiv.org/pdf/2205.15364v4.pdf | Associative Learning Mechanism for Drug-Target Interaction Prediction | As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. ... | ['Baisen Cong', 'Neal Mazur', 'Guanqiu Qi', 'Zheng Yao', 'Zhiqin Zhu'] | 2022-05-24 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 6.60816878e-02 -2.67713964e-01 -6.31758988e-01 -3.99402469e-01
-1.90681905e-01 -1.99690923e-01 2.31300414e-01 3.23995769e-01
-9.69222113e-02 1.05130661e+00 3.40685174e-02 -4.83369499e-01
-6.19541407e-01 -9.48101044e-01 -7.47187555e-01 -1.17180002e+00
-9.91246700e-02 4.55303937e-01 -4.64440770e-02 -1.26599997... | [5.139306545257568, 5.794998645782471] |
11acad6b-94d1-42c0-a990-ad0ea673f63d | hdr-video-reconstruction-a-coarse-to-fine | 2103.14943 | null | https://arxiv.org/abs/2103.14943v2 | https://arxiv.org/pdf/2103.14943v2.pdf | HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset | High dynamic range (HDR) video reconstruction from sequences captured with alternating exposures is a very challenging problem. Existing methods often align low dynamic range (LDR) input sequence in the image space using optical flow, and then merge the aligned images to produce HDR output. However, accurate alignment ... | ['Lei Zhang', 'Kwan-Yee K. Wong', 'Zhetong Liang', 'Shi Guo', 'Chaofeng Chen', 'GuanYing Chen'] | 2021-03-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_HDR_Video_Reconstruction_A_Coarse-To-Fine_Network_and_a_Real-World_Benchmark_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_HDR_Video_Reconstruction_A_Coarse-To-Fine_Network_and_a_Real-World_Benchmark_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-reconstruction', 'hdr-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.80638611e-01 -7.99374223e-01 6.45006597e-02 -1.62663817e-01
-6.38511181e-01 -2.39117891e-01 2.67415941e-01 -5.88976920e-01
-3.10126185e-01 6.62018955e-01 4.64244634e-01 8.95315334e-02
9.85631943e-02 -6.75228238e-01 -7.35582292e-01 -7.52371550e-01
9.15067457e-03 -9.11671892e-02 2.82786727e-01 -2.67239571... | [10.912384033203125, -2.183711528778076] |
9a81e68f-7377-4098-882e-3eb33c6c9413 | iitp-at-semeval-2017-task-8-a-supervised | null | null | https://aclanthology.org/S17-2087 | https://aclanthology.org/S17-2087.pdf | IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation | This paper describes our system participation in the SemEval-2017 Task 8 {`}RumourEval: Determining rumour veracity and support for rumours{'}. The objective of this task was to predict the stance and veracity of the underlying rumour. We propose a supervised classification approach employing several lexical, content a... | ['Md. Shad Akhtar', 'Asif Ekbal', 'Pushpak Bhattacharyya', 'Vikram Singh', 'Sunny Narayan'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-5.01961946e-01 3.55531871e-01 -6.80394113e-01 -2.55697995e-01
-3.97641450e-01 -2.12774619e-01 1.22753310e+00 5.40414453e-01
-2.20420867e-01 1.26301086e+00 7.35562205e-01 -2.48334453e-01
2.64283270e-01 -4.84523088e-01 -4.64875966e-01 -2.16974914e-01
-1.22392982e-01 4.84720528e-01 3.14084768e-01 -8.25207889... | [8.228715896606445, 10.112257957458496] |
4a5d03bf-bbf0-4362-a1e4-40b850d1f27c | use-of-affective-visual-information-for | 2107.03783 | null | https://arxiv.org/abs/2107.03783v1 | https://arxiv.org/pdf/2107.03783v1.pdf | Use of Affective Visual Information for Summarization of Human-Centric Videos | Increasing volume of user-generated human-centric video content and their applications, such as video retrieval and browsing, require compact representations that are addressed by the video summarization literature. Current supervised studies formulate video summarization as a sequence-to-sequence learning problem and ... | ['Engin Erzin', 'Berkay Köprü'] | 2021-07-08 | null | null | null | null | ['supervised-video-summarization'] | ['computer-vision'] | [ 1.05866730e-01 -1.31694376e-01 -1.43115535e-01 -2.18253240e-01
-7.96506047e-01 -1.17471732e-01 5.75520337e-01 2.46626347e-01
-4.02345121e-01 5.07541776e-01 8.66160452e-01 3.94553095e-01
9.17645320e-02 -1.78211927e-01 -6.88873708e-01 -6.27358973e-01
-2.53759455e-02 -5.88131361e-02 -3.06174532e-03 -1.01174213... | [10.446755409240723, 0.44722074270248413] |
61fb7de2-01d1-4dab-a85e-6bb485ce3562 | interactive-feature-embedding-for-infrared | 2211.04877 | null | https://arxiv.org/abs/2211.04877v1 | https://arxiv.org/pdf/2211.04877v1.pdf | Interactive Feature Embedding for Infrared and Visible Image Fusion | General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well designed loss function, which cannot guarantee that all vital information o... | ['Huchuan Lu', 'Wenda Zhao', 'Fan Zhao'] | 2022-11-09 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 2.41108596e-01 -8.33313689e-02 -2.96346724e-01 -1.46837473e-01
-6.47171140e-01 -7.04369843e-02 4.65568811e-01 1.78604782e-01
-2.77697831e-01 5.66385984e-01 1.96868554e-01 -8.73056278e-02
-3.82803887e-01 -8.19332361e-01 -3.32785636e-01 -1.25563693e+00
3.19187671e-01 -4.65925604e-01 -2.72157520e-01 -2.89935470... | [10.488018989562988, -1.9987648725509644] |
dd387519-ba00-42e0-901d-1bb2ffba2eac | generating-high-quality-emotion-arcs-for-low | 2306.02213 | null | https://arxiv.org/abs/2306.02213v1 | https://arxiv.org/pdf/2306.02213v1.pdf | Generating High-Quality Emotion Arcs For Low-Resource Languages Using Emotion Lexicons | Automatically generated emotion arcs -- that capture how an individual or a population feels over time -- are widely used in industry and research. However, there is little work on evaluating the generated arcs in English (where the emotion resources are available) and no work on generating or evaluating emotion arcs f... | ['Saif M. Mohammad', 'Daniela Teodorescu'] | 2023-06-03 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 3.42965536e-02 2.02053174e-01 -4.59721982e-01 -5.19478202e-01
-8.20244491e-01 -7.09082603e-01 4.77231175e-01 1.85406849e-01
-4.30815428e-01 8.56422722e-01 5.13790071e-01 -4.74486798e-01
1.96013168e-01 -6.48375094e-01 -1.07205406e-01 -2.98127353e-01
1.16644002e-01 4.05614913e-01 -6.36171877e-01 -5.08560240... | [12.823442459106445, 6.244893550872803] |
9b8a8b10-a751-40c7-850b-cb873e0615da | multi-head-uncertainty-inference-for | 2212.10006 | null | https://arxiv.org/abs/2212.10006v1 | https://arxiv.org/pdf/2212.10006v1.pdf | Multi-head Uncertainty Inference for Adversarial Attack Detection | Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed in recent years to overcome the adversarial attacks. In this paper, we propose a mu... | ['Kongming Liang', 'Ke Zhang', 'Kai Guo', 'Jiyang Xie. Zhongwei Si', 'Songyun Yang', 'YuQi Yang'] | 2022-12-20 | null | null | null | null | ['adversarial-defense', 'adversarial-attack-detection', 'adversarial-attack-detection'] | ['adversarial', 'computer-vision', 'knowledge-base'] | [-2.98398640e-02 4.68290716e-01 3.99099201e-01 -5.36524653e-01
-7.60840356e-01 -6.61799431e-01 6.80144608e-01 -1.47929639e-01
-1.82351544e-01 9.73200738e-01 4.78082478e-01 2.58933585e-02
7.26004094e-02 -1.04855251e+00 -9.07800555e-01 -8.79366279e-01
1.19579278e-01 5.46164930e-01 4.25580144e-01 -5.95144331... | [5.615905284881592, 7.8759589195251465] |
e794b42a-15b9-452a-91b6-84781c7d44c9 | variational-graph-recurrent-neural-networks | 1908.09710 | null | https://arxiv.org/abs/1908.09710v3 | https://arxiv.org/pdf/1908.09710v3.pdf | Variational Graph Recurrent Neural Networks | Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we develop a novel hierarchical variational model that introduces additional latent random variables to jointly model the hidden states of a graph... | ['Krishna R. Narayanan', 'Xiaoning Qian', 'Ehsan Hajiramezanali', 'Arman Hasanzadeh', 'Nick Duffield', 'Mingyuan Zhou'] | 2019-08-26 | variational-graph-recurrent-neural-networks-1 | http://papers.nips.cc/paper/9254-variational-graph-recurrent-neural-networks | http://papers.nips.cc/paper/9254-variational-graph-recurrent-neural-networks.pdf | neurips-2019-12 | ['dynamic-link-prediction'] | ['graphs'] | [ 2.14690920e-02 7.13122845e-01 -6.71451330e-01 -9.57919136e-02
-3.22820753e-01 -3.98390412e-01 8.05648208e-01 -1.29636645e-01
6.67702496e-01 5.07561982e-01 4.22038168e-01 -4.33728188e-01
-2.17402279e-01 -1.00690091e+00 -6.66532040e-01 -4.95588213e-01
-6.00217223e-01 9.52102184e-01 8.88753235e-02 -1.88761756... | [7.143710136413574, 6.056694507598877] |
45a42885-e58a-4727-9834-1a007ef0c56d | structured-learning-in-time-dependent-cox | 2306.12528 | null | https://arxiv.org/abs/2306.12528v1 | https://arxiv.org/pdf/2306.12528v1.pdf | Structured Learning in Time-dependent Cox Models | Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (i.e., covariat... | ['Mireille E. Schnitzer', 'Marc Dorais', 'Sylvie Perreault', 'Rui Wang', 'Robert W. Platt', 'Archer Y. Yang', 'Yi Lian', 'Guanbo Wang'] | 2023-06-21 | null | null | null | null | ['variable-selection', 'survival-analysis'] | ['methodology', 'miscellaneous'] | [ 8.87499526e-02 -5.42865515e-01 -7.02900589e-01 -5.12578309e-01
-7.88647413e-01 -3.18849236e-01 -1.47776222e-02 3.51876825e-01
-2.29502097e-01 9.91041243e-01 3.72844458e-01 -8.95109713e-01
-5.99226415e-01 -7.67755210e-01 -9.99028832e-02 -6.00203097e-01
-1.11841571e+00 6.33693695e-01 -7.09666014e-02 8.83020386... | [7.728705883026123, 5.284756183624268] |
f4cdf106-2f8f-44d2-b6da-789ddd995956 | assessing-yolact-for-real-time-and-robust | 2103.15997 | null | https://arxiv.org/abs/2103.15997v2 | https://arxiv.org/pdf/2103.15997v2.pdf | Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures | Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of robust models f... | ['Sharib Ali', 'Gilberto Ochoa-Ruiz', 'Leonardo Chang', 'Juan Carlos Angeles Ceron'] | 2021-03-30 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [-3.91002186e-03 2.62730330e-01 -3.72318923e-01 1.57698598e-02
-8.46272647e-01 -6.63760781e-01 2.80982345e-01 1.79310888e-01
-8.14508736e-01 2.87108302e-01 -1.90442309e-01 -4.77112204e-01
7.15194270e-02 -1.16505161e-01 -7.11467803e-01 -4.87972528e-01
-1.09460065e-03 3.57833415e-01 3.07164848e-01 -9.37595889... | [14.049773216247559, -3.207242012023926] |
8277b287-b5e0-4455-aba4-518e07d5272d | document-ai-benchmarks-models-and | 2111.08609 | null | https://arxiv.org/abs/2111.08609v1 | https://arxiv.org/pdf/2111.08609v1.pdf | Document AI: Benchmarks, Models and Applications | Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. It is an important research direction for natural language processing and computer vision. In recent years, the popularity of deep learning ... | ['Furu Wei', 'Tengchao Lv', 'Yiheng Xu', 'Lei Cui'] | 2021-11-16 | null | null | null | null | ['document-image-classification', 'document-layout-analysis', 'document-ai'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.42832297e-01 -2.98698813e-01 -4.42835093e-01 -2.53064930e-01
-2.62477309e-01 -5.97998500e-01 1.06240392e+00 5.86855590e-01
1.17132358e-01 3.56157988e-01 2.04728693e-01 -5.79666376e-01
-2.48169765e-01 -8.68347764e-01 -2.33855247e-01 -5.99613130e-01
9.28232670e-02 5.41138947e-01 -1.45228624e-01 1.71675906... | [11.617058753967285, 2.6606860160827637] |
d90b011d-0690-41b5-b96f-aadd10681d35 | lidar-based-3d-tracking-and-state-estimation | 2304.01396 | null | https://arxiv.org/abs/2304.01396v1 | https://arxiv.org/pdf/2304.01396v1.pdf | Lidar based 3D Tracking and State Estimation of Dynamic Objects | State estimation of oncoming vehicles: Earlier research has been based on determining states like position, velocity, orientation , angular velocity, etc of ego-vehicle. Our approach focuses on estimating the states of non-ego vehicles which is crucial for Motion planning and decision-making. Dynamic Scene Based Locali... | ['Gautham Narayan Narasimhan', 'Patil Shubham Suresh'] | 2023-04-03 | null | null | null | null | ['motion-planning'] | ['robots'] | [-5.83109140e-01 -2.68595874e-01 -3.15941840e-01 -4.55725014e-01
1.44288875e-02 -7.16432154e-01 9.14357245e-01 -1.26485690e-01
-3.18798244e-01 5.94688058e-01 3.15541834e-01 -3.41065586e-01
4.53304797e-01 -7.25698531e-01 -3.56592268e-01 -4.60627466e-01
-5.41029215e-01 4.90301400e-01 7.72912383e-01 -1.98095605... | [6.030456066131592, 0.5716851949691772] |
e7c1ed7a-93ac-436d-b78f-cc5213fdfc5c | stock-trading-volume-prediction-with-dual | 2211.01762 | null | https://arxiv.org/abs/2211.01762v1 | https://arxiv.org/pdf/2211.01762v1.pdf | Stock Trading Volume Prediction with Dual-Process Meta-Learning | Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading. Previous methods mostly learn a universal model for different stocks. However, this kind of practice omits the specific characteristics of individual stocks by applying the ... | ['Xu sun', 'Keiko Harimoto', 'Ruihan Bao', 'Zhiyuan Zhang', 'Wei Li', 'Ruibo Chen'] | 2022-10-11 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-5.32246530e-01 -3.11338902e-01 -7.47660875e-01 -2.15718940e-01
-3.85378599e-01 -4.89314795e-01 5.98269701e-01 4.70459387e-02
-2.40169182e-01 6.67389870e-01 2.37228706e-01 -1.36068135e-01
-3.80614661e-02 -1.12740517e+00 -7.69981444e-01 -6.93074703e-01
1.21644028e-01 6.26608193e-01 3.07068229e-01 1.53340315... | [4.4217753410339355, 4.237804889678955] |
2ea04f81-a234-4937-b867-82f68823c86f | bi-directional-object-context-prioritization | 2203.09416 | null | https://arxiv.org/abs/2203.09416v2 | https://arxiv.org/pdf/2203.09416v2.pdf | Bi-directional Object-context Prioritization Learning for Saliency Ranking | The saliency ranking task is recently proposed to study the visual behavior that humans would typically shift their attention over different objects of a scene based on their degrees of saliency. Existing approaches focus on learning either object-object or object-scene relations. Such a strategy follows the idea of ob... | ['Rynson W. H. Lau', 'BaoCai Yin', 'Lin Du', 'Xin Yang', 'Ke Xu', 'Xin Tian'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tian_Bi-Directional_Object-Context_Prioritization_Learning_for_Saliency_Ranking_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tian_Bi-Directional_Object-Context_Prioritization_Learning_for_Saliency_Ranking_CVPR_2022_paper.pdf | cvpr-2022-1 | ['saliency-ranking'] | ['computer-vision'] | [ 2.48573348e-01 -9.15430859e-02 -2.82818019e-01 -4.22215134e-01
-3.06624472e-01 -1.23814300e-01 6.21093452e-01 3.76393676e-01
-3.06797296e-01 2.19295353e-01 4.29347515e-01 1.09565835e-02
-1.13471538e-01 -6.30978048e-01 -6.89500570e-01 -5.64418137e-01
1.68246806e-01 1.64690316e-01 8.03671420e-01 -2.27768272... | [9.941141128540039, 0.08417186141014099] |
cdd229fc-756c-4446-9a28-66426e27f5f7 | point-based-value-iteration-for-neuro | 2306.17639 | null | https://arxiv.org/abs/2306.17639v1 | https://arxiv.org/pdf/2306.17639v1.pdf | Point-based Value Iteration for Neuro-Symbolic POMDPs | Neuro-symbolic artificial intelligence is an emerging area that combines traditional symbolic techniques with neural networks. In this paper, we consider its application to sequential decision making under uncertainty. We introduce neuro-symbolic partially observable Markov decision processes (NS-POMDPs), which model a... | ['Marta Kwiatkowska', 'David Parker', 'Gethin Norman', 'Gabriel Santos', 'Rui Yan'] | 2023-06-30 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty', 'decision-making'] | ['medical', 'reasoning', 'reasoning'] | [ 2.20030650e-01 7.06577659e-01 -2.17740938e-01 -1.85692832e-01
-2.89531559e-01 -5.51135600e-01 6.94815218e-01 2.33543113e-01
-6.79471672e-01 1.26191747e+00 -1.45555034e-01 -4.66794908e-01
-7.15559423e-01 -9.43931103e-01 -8.31710637e-01 -7.88479924e-01
-4.62640822e-01 1.09530938e+00 2.96951026e-01 -5.75637281... | [4.36722469329834, 2.1570072174072266] |
27602f8b-4925-4e67-b15d-e1b471cc70db | lightea-a-scalable-robust-and-interpretable | 2210.10436 | null | https://arxiv.org/abs/2210.10436v2 | https://arxiv.org/pdf/2210.10436v2.pdf | LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation | Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs. In this paper, we argue that existing GNN-based EA methods inherit the inborn defects from their neural network lineage: weak scalability and poor interpretability. Inspired by re... | ['Man Lan', 'Yuanbin Wu', 'Wenting Wang', 'Xin Mao'] | 2022-10-19 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [ 2.41637975e-01 3.50965947e-01 -4.64598686e-01 -3.36641431e-01
-5.57211757e-01 -4.48020279e-01 1.84047118e-01 2.18896806e-01
-3.73286813e-01 8.50783646e-01 9.50330645e-02 -2.83807486e-01
-2.41563782e-01 -9.67219472e-01 -8.25726688e-01 -4.94886398e-01
-9.95798111e-02 8.72243285e-01 7.12532923e-02 -2.74455369... | [8.800410270690918, 8.035035133361816] |
37e3995b-b8cc-436f-962e-13091f92c073 | validation-loss-for-landmark-detection | 1901.10143 | null | https://arxiv.org/abs/1901.10143v3 | https://arxiv.org/pdf/1901.10143v3.pdf | Learning to Validate the Quality of Detected Landmarks | We present a new loss function for the validation of image landmarks detected via Convolutional Neural Networks (CNN). The network learns to estimate how accurate its landmark estimation is. This loss function is applicable to all regression-based location estimations and allows the exclusion of unreliable landmarks fr... | ['Wolfgang Fuhl', 'Enkelejda Kasneci'] | 2019-01-29 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-4.85843569e-02 1.17132455e-01 -2.40113363e-01 -8.04965556e-01
-1.00743747e+00 -2.49838412e-01 6.53739572e-01 3.77481490e-01
-8.85308683e-01 6.56392574e-01 -1.19143896e-01 8.03767741e-02
-2.01181471e-01 -6.80241644e-01 -8.45310271e-01 -7.17760026e-01
-3.75923932e-01 1.98987544e-01 1.66122571e-01 3.29751432... | [13.34043025970459, 0.6962951421737671] |
98be75d4-baa3-4bc5-bebd-f6da8fafb5fc | an-efficientnet-based-modified-sigmoid | null | null | https://doi.org/10.1016/j.cmpb.2022.106935 | https://www.sciencedirect.com/science/article/pii/S0169260722003170?via%3Dihub | An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions | Background and objective: During the initial stages, skin lesions may not have sufficient intensity difference or contrast from the background region on dermatological macro-images. The lack of proper light exposure at the time of capturing the image also reduces the contrast. Low contrast between lesion and background... | ['Malaya Kumar Nath', 'M. Vipin Das', 'Justin Joseph', 'Vipin Venugopal'] | 2022-07-17 | null | null | null | computer-methods-and-programs-in-biomedicine-3 | ['local-color-enhancement', 'skin-lesion-segmentation'] | ['computer-vision', 'medical'] | [ 6.52666211e-01 -6.53971434e-02 -5.59905432e-02 -2.60220379e-01
-1.57904223e-01 -3.06012124e-01 1.58657283e-01 8.07042494e-02
-7.63319850e-01 7.87670672e-01 -7.26069272e-01 -2.48180285e-01
-6.26023039e-02 -7.77249157e-01 -1.46510214e-01 -1.13027453e+00
3.21465760e-01 -1.33102834e-01 3.94064426e-01 1.56758949... | [15.543158531188965, -2.991095542907715] |
8a3cd836-b2c6-440f-9782-567e1a49ec48 | a-deep-active-contour-model-for-delineating | 2307.03461 | null | https://arxiv.org/abs/2307.03461v1 | https://arxiv.org/pdf/2307.03461v1.pdf | A Deep Active Contour Model for Delineating Glacier Calving Fronts | Choosing how to encode a real-world problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often been approached as a semantic segmentation task. Recent studies have shown that combining segmentation with edge detection can improve the accu... | ['Xiao Xiang Zhu', 'Sébastien Lefèvre', 'Mirko Scheinert', 'Erik Loebel', 'Lichao Mou', 'Konrad Heidler'] | 2023-07-07 | null | null | null | null | ['edge-detection', 'contour-detection'] | ['computer-vision', 'computer-vision'] | [ 1.15684174e-01 4.99733180e-01 9.54753906e-02 -4.79796469e-01
-9.45716739e-01 -7.72852361e-01 6.89537823e-01 3.20870906e-01
-4.14047688e-01 4.15373385e-01 8.09744745e-02 -8.49484861e-01
6.19656630e-02 -9.95790243e-01 -6.66260362e-01 -6.91716254e-01
-2.68441170e-01 4.85226780e-01 2.49871075e-01 -2.28937924... | [9.554694175720215, -1.4818305969238281] |
7aabeb03-7673-4180-966b-b78129894abc | mci-net-multi-scale-context-integrated | null | null | https://www.sciencedirect.com/science/article/pii/S0045790622003408 | https://www.sciencedirect.com/science/article/pii/S0045790622003408 | Mci-net: multi-scale context integrated network for liver ct image segmentation | Owing to the various object scales and high similarity with the surrounding organs (e.g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images. In this study, we propose a multi-scale context integration network called MCI-Net for liver image... | ['Jubai An', 'Weidong Zhang', 'Feng Shao', 'Xipeng Pan', 'Xiwang Xie'] | 2023-05-03 | null | null | null | computers-and-electrical-engineering-2023-5 | ['2d-semantic-segmentation', 'liver-segmentation'] | ['computer-vision', 'medical'] | [-1.79897919e-01 -4.63910289e-02 -1.58635795e-01 -4.31401879e-01
-4.26436633e-01 -3.55300605e-01 1.14042036e-01 1.89149871e-01
-3.25343221e-01 3.58000040e-01 3.54225993e-01 -1.93432719e-01
1.15438499e-01 -5.22567272e-01 -4.25259382e-01 -8.12698543e-01
-1.64517134e-01 -1.09193549e-01 4.80496436e-01 1.39206171... | [14.594420433044434, -2.550635576248169] |
0c42a7e6-c897-4152-928b-3358b2c5cf3e | msc-a-dataset-for-macro-management-in | 1710.03131 | null | https://arxiv.org/abs/1710.03131v3 | https://arxiv.org/pdf/1710.03131v3.pdf | MSC: A Dataset for Macro-Management in StarCraft II | Macro-management is an important problem in StarCraft, which has been studied for a long time. Various datasets together with assorted methods have been proposed in the last few years. But these datasets have some defects for boosting the academic and industrial research: 1) There're neither standard preprocessing, par... | ['Kaiqi Huang', 'Junge Zhang', 'Yanqi Zong', 'Huikai Wu'] | 2017-10-09 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.65391320e-01 -4.64095652e-01 -6.65073276e-01 -4.77643728e-01
-2.22198665e-01 -7.17421949e-01 5.75027287e-01 1.72898889e-01
-2.79647589e-01 5.87027609e-01 3.24642807e-01 -1.47311345e-01
-2.10452318e-01 -9.55193818e-01 -4.60618138e-01 -5.14041901e-01
-4.81609553e-02 6.76219642e-01 6.22432649e-01 -8.20169568... | [4.086453437805176, 1.5883162021636963] |
49eb609b-3857-42ea-bf91-b842d5cf7524 | temporal-relational-modeling-with-self | 2012.07508 | null | https://arxiv.org/abs/2012.07508v1 | https://arxiv.org/pdf/2012.07508v1.pdf | Temporal Relational Modeling with Self-Supervision for Action Segmentation | Temporal relational modeling in video is essential for human action understanding, such as action recognition and action segmentation. Although Graph Convolution Networks (GCNs) have shown promising advantages in relation reasoning on many tasks, it is still a challenge to apply graph convolution networks on long video... | ['Dejing Dou', 'Xingjian Li', 'Di Hu', 'Dong Wang'] | 2020-12-14 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 1.01047894e-02 4.35526818e-02 -4.21611369e-01 -3.00241530e-01
1.16766594e-01 -2.94447243e-01 4.22656178e-01 -2.04069108e-01
-2.09941268e-01 3.25269639e-01 2.40013063e-01 -2.78180003e-01
-3.19416434e-01 -6.99365735e-01 -6.77455008e-01 -4.78415936e-01
-2.55943418e-01 1.51190490e-01 6.41607642e-01 -1.06229633... | [8.536276817321777, 0.6374086737632751] |
b72f6813-d83c-4a0e-b922-73a8a612e18d | video-story-composition-via-plot-analysis | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Choi_Video-Story_Composition_via_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Choi_Video-Story_Composition_via_CVPR_2016_paper.pdf | Video-Story Composition via Plot Analysis | We address the problem of composing a story out of multiple short video clips taken by a person during an activity or experience. Inspired by plot analysis of written stories, our method generates a sequence of video clips ordered in such a way that it reflects plot dynamics and content coherency. That is, given a set ... | ['Tae-Hyun Oh', 'Jinsoo Choi', 'In So Kweon'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['patch-matching'] | ['computer-vision'] | [ 2.17058927e-01 -4.01135236e-01 -1.70561448e-01 -2.02203929e-01
-5.63840806e-01 -7.72031724e-01 4.29796189e-01 1.90441176e-01
6.95777461e-02 3.40014428e-01 6.66548431e-01 2.78637171e-01
-2.28068128e-01 -6.05236828e-01 -7.21402645e-01 -3.75368774e-01
-2.70622522e-01 -1.49924144e-01 2.61996388e-01 1.05352320... | [10.572590827941895, 0.5877353549003601] |
b720c432-1d70-4f63-9480-8b89652e616a | learning-based-model-predictive-control-for | 1803.08287 | null | http://arxiv.org/abs/1803.08287v3 | http://arxiv.org/pdf/1803.08287v3.pdf | Learning-based Model Predictive Control for Safe Exploration | Learning-based methods have been successful in solving complex control tasks
without significant prior knowledge about the system. However, these methods
typically do not provide any safety guarantees, which prevents their use in
safety-critical, real-world applications. In this paper, we present a
learning-based model... | ['Andreas Krause', 'Torsten Koller', 'Matteo Turchetta', 'Felix Berkenkamp'] | 2018-03-22 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.59368053e-01 3.04628342e-01 -3.70559722e-01 1.77004606e-01
-8.00562918e-01 -6.94705069e-01 4.84627217e-01 5.88100553e-01
-1.31413132e-01 9.81782079e-01 -4.54455227e-01 -6.61007702e-01
-4.48783010e-01 -7.79555559e-01 -1.03248382e+00 -7.94063628e-01
-3.64484370e-01 4.37976539e-01 4.57239538e-01 3.53652537... | [4.821493148803711, 2.2602005004882812] |
c373f7d5-8c52-4633-8c1f-7441a24046a5 | pretraining-the-noisy-channel-model-for-task | 2103.10518 | null | https://arxiv.org/abs/2103.10518v1 | https://arxiv.org/pdf/2103.10518v1.pdf | Pretraining the Noisy Channel Model for Task-Oriented Dialogue | Direct decoding for task-oriented dialogue is known to suffer from the explaining-away effect, manifested in models that prefer short and generic responses. Here we argue for the use of Bayes' theorem to factorize the dialogue task into two models, the distribution of the context given the response, and the prior for t... | ['Phil Blunsom', 'Laura Rimell', 'Lei Yu', 'Qi Liu'] | 2021-03-18 | null | null | null | null | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [ 5.61890662e-01 7.40145445e-01 1.28961533e-01 -8.96346092e-01
-1.17489803e+00 -6.46313787e-01 8.75387788e-01 -8.94662924e-03
-5.97311258e-01 9.67288673e-01 9.53862846e-01 -5.41293085e-01
1.44612908e-01 -4.28348631e-01 -2.27057844e-01 -2.79491931e-01
2.78345019e-01 7.99800098e-01 1.11191563e-01 -5.84683776... | [12.876802444458008, 7.950674533843994] |
5eadf271-65dd-4f9f-99e1-8a84858e4cac | inertial-navigation-meets-deep-learning-a | 2307.00014 | null | https://arxiv.org/abs/2307.00014v1 | https://arxiv.org/pdf/2307.00014v1.pdf | Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions | Inertial sensing is used in many applications and platforms, ranging from day-to-day devices such as smartphones to very complex ones such as autonomous vehicles. In recent years, the development of machine learning and deep learning techniques has increased significantly in the field of inertial sensing. This is due t... | ['Itzik Klein', 'Nadav Cohen'] | 2023-06-22 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-1.44861177e-01 -2.59095818e-01 -2.48691723e-01 -4.53145832e-01
-6.46628320e-01 -1.75233513e-01 4.29895252e-01 -2.35054210e-01
-5.79551637e-01 9.13561344e-01 3.14887226e-01 -4.23349589e-01
2.04991564e-01 -9.30398941e-01 -8.79446924e-01 -6.94598734e-01
-6.93292022e-02 -1.37283653e-02 -2.15912253e-01 -4.18521523... | [7.459315299987793, -1.9654712677001953] |
31234063-7e8d-4105-8496-270c7b8f33f9 | it-is-all-connected-a-new-graph-formulation | 2303.13177 | null | https://arxiv.org/abs/2303.13177v1 | https://arxiv.org/pdf/2303.13177v1.pdf | It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting | With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most spatio-temporal forecasting models typically comprise distinct components that learn spatial and temporal dependencies. A common methodology e... | ['Paal Engelstad', 'Roy Stenbro', 'Narada Dilp Warakagoda', 'Lars Ødegaard Bentsen'] | 2023-03-23 | null | null | null | null | ['irregular-time-series', 'spatio-temporal-forecasting'] | ['time-series', 'time-series'] | [ 4.80739353e-03 -1.11627869e-01 -2.02954888e-01 -2.59428561e-01
9.02808979e-02 -4.95290488e-01 9.20835078e-01 4.94645536e-01
-2.85370648e-01 7.84015238e-01 3.11520278e-01 -5.88715434e-01
-4.96776253e-01 -1.25499940e+00 -7.29803681e-01 -7.23583877e-01
-6.38122499e-01 1.25289723e-01 9.48994532e-02 -1.58683553... | [6.63401985168457, 2.748762369155884] |
00a29c3f-8a18-49f7-87b4-b4ecf1f2bcca | segnerf-3d-part-segmentation-with-neural | 2211.11215 | null | https://arxiv.org/abs/2211.11215v2 | https://arxiv.org/pdf/2211.11215v2.pdf | SegNeRF: 3D Part Segmentation with Neural Radiance Fields | Recent advances in Neural Radiance Fields (NeRF) boast impressive performances for generative tasks such as novel view synthesis and 3D reconstruction. Methods based on neural radiance fields are able to represent the 3D world implicitly by relying exclusively on posed images. Yet, they have seldom been explored in the... | ['Bernard Ghanem', 'Silvio Giancola', 'Sara Rojas', 'Jesus Zarzar'] | 2022-11-21 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [ 3.73756438e-01 4.06269729e-01 2.44142056e-01 -5.54969251e-01
-7.05548584e-01 -5.26348531e-01 6.41471267e-01 -3.04636598e-01
-4.03571948e-02 3.89942795e-01 -3.11251104e-01 -5.90811782e-02
8.96812975e-03 -1.16188097e+00 -1.19886625e+00 -4.70805466e-01
2.85000861e-01 6.17601812e-01 2.38062903e-01 -3.93006891... | [8.560770988464355, -3.2679786682128906] |
f1b83af9-924c-4601-96c2-f7d6357162da | distance-surface-for-event-based-optical-flow | 2003.12680 | null | https://arxiv.org/abs/2003.12680v1 | https://arxiv.org/pdf/2003.12680v1.pdf | Distance Surface for Event-Based Optical Flow | We propose DistSurf-OF, a novel optical flow method for neuromorphic cameras. Neuromorphic cameras (or event detection cameras) are an emerging sensor modality that makes use of dynamic vision sensors (DVS) to report asynchronously the log-intensity changes (called "events") exceeding a predefined threshold at each pix... | ['Mohammed Almatrafi', 'Raymond Baldwin', 'Kiyoharu Aizawa', 'Keigo Hirakawa'] | 2020-03-28 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 5.81760406e-01 -5.80328286e-01 1.90844476e-01 -1.95183367e-01
-2.24199910e-02 -8.35409522e-01 4.79398191e-01 -2.54924633e-02
-9.32836294e-01 6.85852110e-01 -1.38638675e-01 3.76475662e-01
1.66915745e-01 -6.60662353e-01 -8.99811208e-01 -8.70158255e-01
-1.10872343e-01 -3.25154930e-01 6.19906843e-01 4.17274773... | [8.666570663452148, -1.2707784175872803] |
7b09db9b-4236-4ee2-9bf1-aca1998d645e | towards-classification-of-legal | null | null | https://aclanthology.org/2022.csrnlp-1.8 | https://aclanthology.org/2022.csrnlp-1.8.pdf | Towards Classification of Legal Pharmaceutical Text using GAN-BERT | Pharmaceutical text classification is an important area of research for commercial and research institutions working in the pharmaceutical domain. Addressing this task is challenging due to the need of expert verified labelled data which can be expensive and time consuming to obtain. Towards this end, we leverage predi... | ['John P. McCrae', 'Vall Herard', 'John Mariano', 'Jay Megaro', 'Michaela Comerford', 'Arindam Paul', 'Atul Kr. Ojha', 'Bernardo Stearns', 'Rajdeep Sarkar', 'Tapan Auti'] | null | null | null | null | csrnlp-lrec-2022-6 | ['sentence-classification'] | ['natural-language-processing'] | [ 5.17977595e-01 2.34439984e-01 -8.90914351e-02 -4.49999005e-01
-1.03675568e+00 -5.83517134e-01 7.09919870e-01 2.81798810e-01
-2.70682693e-01 8.32329631e-01 2.71510422e-01 -6.82741225e-01
1.92123074e-02 -4.01207298e-01 -7.44354784e-01 -4.64048237e-01
2.17783391e-01 5.47839105e-01 -2.53822744e-01 -1.17672838... | [8.605764389038086, 8.498472213745117] |
379dc4b1-de9c-41b9-89b5-aebe178934d9 | multi-object-tracking-and-segmentation-with-a | 2110.11284 | null | https://arxiv.org/abs/2110.11284v2 | https://arxiv.org/pdf/2110.11284v2.pdf | Multi-Object Tracking and Segmentation with a Space-Time Memory Network | We propose a method for multi-object tracking and segmentation based on a novel memory-based mechanism to associate tracklets. The proposed tracker, MeNToS, addresses particularly the long-term data association problem, when objects are not observable for long time intervals. Indeed, the recently introduced HOTA metric... | ['Nicolas Saunier', 'Guillaume-Alexandre Bilodeau', 'Mehdi Miah'] | 2021-10-21 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [-1.37099713e-01 -3.36217046e-01 -1.03219196e-01 7.10674152e-02
-3.55815321e-01 -7.26593733e-01 4.64004338e-01 2.70851791e-01
-6.13334119e-01 6.91980600e-01 -5.67324758e-01 -1.23428879e-02
-5.47423005e-01 -4.76286352e-01 -8.07276607e-01 -4.88467187e-01
-3.15421253e-01 7.60052681e-01 1.03484476e+00 1.52325973... | [6.514688014984131, -2.0205962657928467] |
60b05cec-2056-42a5-b19b-2be06673c2ce | knowledge-guided-paraphrase-identification | null | null | https://aclanthology.org/2021.findings-emnlp.72 | https://aclanthology.org/2021.findings-emnlp.72.pdf | Knowledge-Guided Paraphrase Identification | Paraphrase identification (PI), a fundamental task in natural language processing, is to identify whether two sentences express the same or similar meaning, which is a binary classification problem. Recently, BERT-like pre-trained language models have been a popular choice for the frameworks of various PI models, but a... | ['Jing Gao', 'Yaqing Wang', 'Fenglong Ma', 'Haoyu Wang'] | null | null | null | null | findings-emnlp-2021-11 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 4.37060118e-01 -3.71067040e-02 -5.12085259e-01 -3.98991585e-01
-8.11851680e-01 -3.41239303e-01 3.36315691e-01 6.30832374e-01
-4.88127381e-01 7.69831181e-01 4.97908026e-01 -1.99526981e-01
-3.67190242e-02 -7.82803237e-01 -6.23027623e-01 -1.85530111e-01
5.75874805e-01 4.19490576e-01 4.17627275e-01 -2.03083187... | [10.995865821838379, 8.440045356750488] |
32115980-47d5-430f-8229-04f7cb5c42df | boosting-camouflaged-object-detection-with | 2205.10579 | null | https://arxiv.org/abs/2205.10579v1 | https://arxiv.org/pdf/2205.10579v1.pdf | Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer | Camouflaged object detection intends to discover the concealed objects hidden in the surroundings. Existing methods follow the bio-inspired framework, which first locates the object and second refines the boundary. We argue that the discovery of camouflaged objects depends on the recurrent search for the object and the... | ['Wei Wu', 'Zhili Zhang', 'Zhengyi Liu'] | 2022-05-21 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 2.86334038e-01 -1.87973112e-01 -2.44924799e-02 1.19231403e-01
-5.22666633e-01 -5.42455196e-01 3.81728470e-01 -4.30289477e-01
-2.19970435e-01 5.48259735e-01 1.62050985e-02 4.62497286e-02
1.33827686e-01 -5.76176405e-01 -4.49362546e-01 -1.28812337e+00
3.78860354e-01 2.99162894e-01 8.35720718e-01 8.67548212... | [9.507146835327148, -0.2824445068836212] |
351f6760-b21c-447f-adaf-515849549c6f | multi-scale-attention-guided-pose-transfer | 2202.06777 | null | https://arxiv.org/abs/2202.06777v1 | https://arxiv.org/pdf/2202.06777v1.pdf | Multi-scale Attention Guided Pose Transfer | Pose transfer refers to the probabilistic image generation of a person with a previously unseen novel pose from another image of that person having a different pose. Due to potential academic and commercial applications, this problem is extensively studied in recent years. Among the various approaches to the problem, a... | ['Umapada Pal', 'Subhankar Ghosh', 'Saumik Bhattacharya', 'Prasun Roy'] | 2022-02-14 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 4.85456318e-01 4.16638136e-01 2.57450670e-01 -2.06332833e-01
-9.32101011e-01 -2.51994759e-01 7.28511155e-01 -2.67381221e-01
-4.03068691e-01 9.43786323e-01 5.07155776e-01 4.99232650e-01
2.36954242e-02 -5.93476236e-01 -8.93416584e-01 -3.67791295e-01
-2.83069797e-02 6.70887291e-01 -1.65531542e-02 -2.11965114... | [11.906764030456543, -0.7405266761779785] |
76747dc6-e537-4a79-b1ab-6d8fb16aaae1 | wppg-net-a-non-contact-video-based-heart-rate | 2207.01697 | null | https://arxiv.org/abs/2207.01697v2 | https://arxiv.org/pdf/2207.01697v2.pdf | BYHE: A Simple Framework for Boosting End-to-end Video-based Heart Rate Measurement Network | Heart rate measuring based on remote photoplethysmography (rPPG) plays an important role in health caring, which estimates heart rate from facial video in a non-contact, less-constrained way. End-to-end neural network is a main branch of rPPG-based heart rate estimation methods, whose trait is recovering rPPG signal co... | ['Xiaolin Huang', 'Chunyu Ji', 'Yun Ge', 'Ying Chen', 'Xinyu Zhang', 'Weiyu Sun'] | 2022-07-04 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.55840367e-01 5.49206464e-03 -3.03820461e-01 -4.01109844e-01
-2.80763239e-01 -1.11492388e-01 -1.70792654e-01 -6.35098100e-01
-2.29855314e-01 8.18348944e-01 5.31419972e-03 2.55774852e-04
-1.21002316e-01 -3.60704660e-01 1.05520887e-02 -8.98186386e-01
-1.29504308e-01 -1.70326829e-01 -3.11322927e-01 -1.97481677... | [13.896801948547363, 2.730717897415161] |
8a079845-31f7-4a5e-894e-15557e77c214 | performance-of-data-driven-inner-speech | 2306.10854 | null | https://arxiv.org/abs/2306.10854v1 | https://arxiv.org/pdf/2306.10854v1.pdf | Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models | Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models. Two different bimodal fusion approaches are examined: concatenation of probability vectors output from unimodal fMRI and EEG machine learning models, and data fusi... | ['Benjamin Metcalfe', "Eamonn O'Neill", 'Marcus Liwicki', 'Michael J. Proulx', 'Mohammad Golbabaee', 'Xi Chen', 'Oliver Watts', 'Johan Eriksson', 'Sumit Rakesh', 'Nosheen Abid', 'Kanjar De', 'Rajkumar Saini', 'Vibha Gupta', 'Foteini Simistira Liwicki', 'Scott Wellington', 'Holly Wilson'] | 2023-06-19 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 5.96361339e-01 1.74702495e-01 5.64410806e-01 -5.59147358e-01
-1.08759773e+00 -1.97829157e-01 9.27925110e-01 8.41942430e-02
-5.28581977e-01 8.91768456e-01 6.39498174e-01 -8.62593651e-02
-4.46243256e-01 2.07886904e-01 -3.23907286e-01 -8.60083818e-01
-2.46270612e-01 7.39729777e-02 -4.15263772e-01 4.03177850... | [13.007132530212402, 3.43220853805542] |
b1d2034a-8eda-4306-b357-041b4d2736e1 | quantifying-the-robustness-of-deep | 2305.11347 | null | https://arxiv.org/abs/2305.11347v1 | https://arxiv.org/pdf/2305.11347v1.pdf | Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning | In overhead image segmentation tasks, including additional spectral bands beyond the traditional RGB channels can improve model performance. However, it is still unclear how incorporating this additional data impacts model robustness to adversarial attacks and natural perturbations. For adversarial robustness, the addi... | ['Eleanor Byler', 'Myles Mckay', 'Charles Godfrey', 'Elise Bishoff'] | 2023-05-18 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 5.20433724e-01 -1.77036032e-01 3.78194213e-01 1.39151484e-01
-4.73917037e-01 -1.30953026e+00 5.33379376e-01 1.45466119e-01
-3.62553328e-01 5.14492273e-01 -8.38764906e-02 -7.25576520e-01
-2.29591993e-03 -9.46514368e-01 -8.57112348e-01 -9.71777916e-01
-3.16303619e-03 -8.54405388e-02 2.85305351e-01 -5.85821450... | [5.477110862731934, 7.953195095062256] |
7af493b7-9d06-487d-bc77-9e51fe416e5d | cyber-risk-frequency-severity-and-insurance | 2111.03366 | null | https://arxiv.org/abs/2111.03366v2 | https://arxiv.org/pdf/2111.03366v2.pdf | Cyber Risk Frequency, Severity and Insurance Viability | In this study an exploration of insurance risk transfer is undertaken for the cyber insurance industry in the United States of America, based on the leading industry dataset of cyber events provided by Advisen. We seek to address two core unresolved questions. First, what factors are the most significant covariates tha... | ['Georgy Sofronov', 'Jiwook Jang', 'Stefan Trück', 'Pavel V. Shevchenko', 'Gareth W. Peters', 'Matteo Malavasi'] | 2021-11-05 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.21207604e-01 5.57901084e-01 -2.51264840e-01 1.30342871e-01
-6.41021967e-01 -7.79334843e-01 4.45350677e-01 4.57663924e-01
-2.28657290e-01 5.70448697e-01 5.01438558e-01 -1.07360280e+00
-7.71156728e-01 -1.03807878e+00 -5.43863714e-01 -8.94221589e-02
1.16062261e-01 1.05430722e-01 -7.51804486e-02 -2.64341205... | [5.8678178787231445, 4.422704219818115] |
647d46fe-25dd-4754-8bb8-45cc27210aa3 | dialogxl-all-in-one-xlnet-for-multi-party | 2012.08695 | null | https://arxiv.org/abs/2012.08695v1 | https://arxiv.org/pdf/2012.08695v1.pdf | DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition | This paper presents our pioneering effort for emotion recognition in conversation (ERC) with pre-trained language models. Unlike regular documents, conversational utterances appear alternately from different parties and are usually organized as hierarchical structures in previous work. Such structures are not conducive... | ['Zhixian Xie', 'Xiaojun Quan', 'Junqing Chen', 'Weizhou Shen'] | 2020-12-16 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-7.21086934e-02 2.31459126e-01 5.27807102e-02 -8.01577806e-01
-6.09883010e-01 -3.19310218e-01 5.97683012e-01 1.31860882e-01
-5.18664479e-01 8.96425664e-01 8.74851823e-01 -2.61795104e-01
4.20405686e-01 -2.51385093e-01 -1.28732547e-01 -5.08327603e-01
-5.03993407e-02 3.44874233e-01 -2.36924127e-01 -5.17086864... | [12.95085620880127, 6.373098373413086] |
d36aec68-17ac-4b17-8b12-c734f08a667f | a-study-of-graph-based-approaches-for-semi | 2104.08153 | null | https://arxiv.org/abs/2104.08153v2 | https://arxiv.org/pdf/2104.08153v2.pdf | An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification | Time series data play an important role in many applications and their analysis reveals crucial information for understanding the underlying processes. Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data. Two main aspects a... | ['Martin Stoll', 'Lucile Peroche', 'Miriam Gondos', 'Dominik Alfke'] | 2021-04-16 | null | null | null | null | ['semi-supervised-time-series-classification'] | ['time-series'] | [ 1.47849396e-01 1.23897217e-01 -1.34708181e-01 -2.19101369e-01
-1.87986001e-01 -6.01975024e-01 8.80564392e-01 9.01005387e-01
-3.77731442e-01 3.80677462e-01 -2.18754280e-02 -5.30491173e-01
-7.87020922e-01 -9.19325411e-01 -2.85037637e-01 -9.01057363e-01
-8.52527320e-01 4.46586847e-01 2.78033793e-01 -4.80117261... | [7.393445014953613, 3.6275107860565186] |
a2448a22-6e11-4b7f-9e2b-c51b6e9ea865 | an-intelligent-mechanism-for-monitoring-and | 2306.17187 | null | https://arxiv.org/abs/2306.17187v1 | https://arxiv.org/pdf/2306.17187v1.pdf | An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices | The current amount of IoT devices and their limitations has come to serve as a motivation for malicious entities to take advantage of such devices and use them for their own gain. To protect against cyberattacks in IoT devices, Machine Learning techniques can be applied to Intrusion Detection Systems. Moreover, privacy... | ['Carlos Bento', 'Paulo Silva', 'Vitalina Holubenko'] | 2023-06-23 | null | null | null | null | ['intrusion-detection'] | ['miscellaneous'] | [-4.49777618e-02 2.31868222e-01 -6.47409201e-01 -5.01141429e-01
-1.86185017e-01 -8.35515976e-01 4.43144530e-01 3.22796226e-01
-3.89544755e-01 5.58248162e-01 -1.22419581e-01 -7.55253494e-01
-4.02125157e-03 -9.91433203e-01 -4.41726416e-01 -4.88235682e-01
2.06669830e-02 -6.21013790e-02 6.97618574e-02 1.16210088... | [5.366448879241943, 7.141229629516602] |
b3ecf7d5-08b0-40bb-9254-e26ff1426c95 | semi-supervised-deep-quick-instance-detection | 2101.06405 | null | https://arxiv.org/abs/2101.06405v1 | https://arxiv.org/pdf/2101.06405v1.pdf | Semi Supervised Deep Quick Instance Detection and Segmentation | In this paper, we present a semi supervised deep quick learning framework for instance detection and pixel-wise semantic segmentation of images in a dense clutter of items. The framework can quickly and incrementally learn novel items in an online manner by real-time data acquisition and generating corresponding ground... | ['L. Behera', 'Ashish Kumar'] | 2021-01-16 | null | null | null | null | ['class-agnostic-object-detection'] | ['computer-vision'] | [ 3.53252739e-01 3.67736876e-01 3.01590413e-01 -6.71593904e-01
-6.96852684e-01 -8.59038770e-01 5.64583123e-01 2.48566344e-01
-6.49841189e-01 8.44268739e-01 -4.63023424e-01 1.50301661e-02
-6.87269121e-02 -7.61725008e-01 -1.22312069e+00 -2.95262963e-01
-2.22391620e-01 8.99244905e-01 6.65775776e-01 1.02958135... | [9.401999473571777, 0.30036288499832153] |
1c526b4d-31d4-4b06-855e-77d33bd2bd3f | re-thinking-co-salient-object-detection | 2007.03380 | null | https://arxiv.org/abs/2007.03380v4 | https://arxiv.org/pdf/2007.03380v4.pdf | Re-thinking Co-Salient Object Detection | In this paper, we conduct a comprehensive study on the co-salient object detection (CoSOD) problem for images. CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images. However, existing CoSOD datasets often have a s... | ['Ming-Ming Cheng', 'Ge-Peng Ji', 'Tengpeng Li', 'Deng-Ping Fan', 'Huazhu Fu', 'Dingwen Zhang', 'Zheng Lin', 'Jianbing Shen'] | 2020-07-07 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 5.67008890e-02 -1.43742412e-01 -2.92813540e-01 -6.09444752e-02
-5.41360497e-01 -2.66119868e-01 6.30034626e-01 1.72244478e-02
-2.39971235e-01 4.07981843e-01 6.11881435e-01 1.13446638e-01
1.22604772e-01 -4.37547773e-01 -7.19833374e-01 -6.10816658e-01
1.63656637e-01 -4.15656716e-02 5.89273751e-01 -1.78044409... | [9.690970420837402, -0.20385970175266266] |
c534b811-9564-4665-851f-27ff9edd703f | deep-image-orientation-angle-detection | 2007.06709 | null | https://arxiv.org/abs/2007.06709v1 | https://arxiv.org/pdf/2007.06709v1.pdf | Deep Image Orientation Angle Detection | Estimating and rectifying the orientation angle of any image is a pretty challenging task. Initial work used the hand engineering features for this purpose, where after the invention of deep learning using convolution-based neural network showed significant improvement in this problem. However, this paper shows that th... | ['Subhadip Maji', 'Smarajit Bose'] | 2020-06-21 | null | null | null | null | ['natural-image-orientation-angle-detection'] | ['computer-vision'] | [-2.82793939e-02 8.95108357e-02 2.30702773e-01 -4.31223243e-01
-1.30946159e-01 -5.13668180e-01 4.01680946e-01 -3.40669334e-01
-4.00299519e-01 5.88710070e-01 -2.10270882e-01 -3.83235484e-01
-4.76051390e-01 -7.32282639e-01 -6.21586144e-01 -6.22532785e-01
-7.60137364e-02 2.46387944e-01 -9.64515582e-02 -4.82142031... | [9.900273323059082, 0.06649274379014969] |
0b769ffe-d308-4f36-86ec-35777b2fada3 | a-wearable-ecg-monitor-for-deep-learning | 2201.10083 | null | https://arxiv.org/abs/2201.10083v1 | https://arxiv.org/pdf/2201.10083v1.pdf | A Wearable ECG Monitor for Deep Learning Based Real-Time Cardiovascular Disease Detection | Cardiovascular disease has become one of the most significant threats endangering human life and health. Recently, Electrocardiogram (ECG) monitoring has been transformed into remote cardiac monitoring by Holter surveillance. However, the widely used Holter can bring a great deal of discomfort and inconvenience to the ... | ['Lu Meng', 'Yang song', 'Ming Ding', 'Zijiao Chen', 'Xucun Yan', 'Zihuai Lin', 'Peng Wang'] | 2022-01-25 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 1.93287984e-01 -9.45805460e-02 2.05639422e-01 -3.79229724e-01
-5.54233432e-01 -2.42634997e-01 -9.30381939e-02 3.02516311e-01
-5.17760336e-01 9.43110049e-01 -3.17839056e-01 -5.46623051e-01
-9.13194418e-02 -8.57140839e-01 -2.17423871e-01 -6.80097818e-01
-3.47795069e-01 6.64372370e-02 -2.73993194e-01 1.78020313... | [14.274114608764648, 3.2542214393615723] |
0f21e511-3d45-44b6-a889-c3a64fefbe07 | neuromorphic-computing-for-content-based | 2008.01380 | null | https://arxiv.org/abs/2008.01380v2 | https://arxiv.org/pdf/2008.01380v2.pdf | Neuromorphic Computing for Content-based Image Retrieval | Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency. Here, we explore the application of Loihi, a neuromorphic computing chip deve... | ['Te-Yuan Liu', 'Daniel Prusinski', 'Luis Stevens', 'Ata Mahjoubfar'] | 2020-08-04 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.89998522e-01 -4.13048834e-01 2.70381153e-01 -6.46125153e-02
1.63725406e-01 -3.75456840e-01 5.44517696e-01 1.21906232e-02
-9.74253237e-01 3.35791677e-01 -2.73507833e-01 -3.24260175e-01
-6.97486475e-02 -9.41837728e-01 -7.02498794e-01 -5.92216313e-01
-1.30789235e-01 1.49675146e-01 1.94564775e-01 3.89305130... | [8.257905006408691, 2.483374834060669] |
dc45eb96-c3eb-4aa5-bbb3-ed7d35971f94 | re-move-an-adaptive-policy-design-approach | 2303.07622 | null | https://arxiv.org/abs/2303.07622v1 | https://arxiv.org/pdf/2303.07622v1.pdf | RE-MOVE: An Adaptive Policy Design Approach for Dynamic Environments via Language-Based Feedback | Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may result in catastrophic failures. To address this limitation, we propose a novel approach called RE-MOVE (\textbf{RE}quest help and \textbf{MOVE} ... | ['Dinesh Manocha', 'Amrit Singh Bedi', 'Pratap Tokekar', 'Prithvi Poddar', 'Kasun Weerakoon', 'Souradip Chakraborty'] | 2023-03-14 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 1.06037192e-01 5.03112614e-01 2.72991568e-01 -2.40549609e-01
-5.99778950e-01 -8.32549632e-01 5.68558633e-01 -5.82102537e-02
-9.88243043e-01 1.30121386e+00 -1.05826959e-01 -4.07024443e-01
-1.51674166e-01 -7.64138341e-01 -1.05537200e+00 -5.66942930e-01
-3.46174657e-01 5.26697755e-01 5.01731575e-01 -5.72657287... | [4.398383140563965, 1.6482456922531128] |
4f70ddc5-f801-4a4c-a901-6bcc5e1710e8 | prompt-tuning-based-adapter-for-vision | 2303.15234 | null | https://arxiv.org/abs/2303.15234v1 | https://arxiv.org/pdf/2303.15234v1.pdf | Prompt Tuning based Adapter for Vision-Language Model Adaption | Large pre-trained vision-language (VL) models have shown significant promise in adapting to various downstream tasks. However, fine-tuning the entire network is challenging due to the massive number of model parameters. To address this issue, efficient adaptation methods such as prompt tuning have been proposed. We exp... | ['Changyou Chen', 'Zihao Lin', 'Jiayu Qin', 'Jingchen Sun'] | 2023-03-24 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [-2.53490475e-03 -2.87420988e-01 -4.47357893e-01 -3.76660109e-01
-7.71377087e-01 -3.72712553e-01 7.84773171e-01 -2.21520349e-01
-7.12526441e-01 5.24017930e-01 3.86005431e-01 -1.27504066e-01
2.23809153e-01 -3.06714952e-01 -7.17771828e-01 -4.82654691e-01
5.62265098e-01 4.10966486e-01 3.88771772e-01 -1.79635212... | [10.088028907775879, 2.2586004734039307] |
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